Ensembl_Intrductiono

合集下载

神经网络传统剪枝算法的流程

神经网络传统剪枝算法的流程英文回答：Neural network pruning is a technique used to reduce the size of a neural network by removing unnecessary connections or neurons. It aims to improve the efficiency and computational speed of the network withoutsignificantly sacrificing its performance. There are several traditional pruning algorithms that have been developed over the years, each with its own specific process.One common pruning algorithm is weight pruning, which involves identifying and removing connections with low weights. The process typically starts with training a neural network to its full capacity. Then, the weights of the connections are ranked based on their magnitudes, and a threshold is set to determine which weights are considered low. Connections with weights below the threshold are pruned, and the resulting network is fine-tuned tocompensate for the removed connections. This process is repeated iteratively until the desired level of pruning is achieved.Another pruning algorithm is neuron pruning, which focuses on removing entire neurons from the network. Neurons are typically ranked based on their importance or contribution to the network's output. The least important neurons, often those with low activation values or low connection weights, are pruned. The network is then retrained to adjust the remaining connections and ensure the preservation of its performance.Both weight pruning and neuron pruning can be combined to achieve even more significant reduction in network size. The process involves alternating between weight pruning and neuron pruning, iteratively removing connections and neurons until the desired level of pruning is achieved.It is worth mentioning that pruning can also be guided by other criteria, such as the network's sensitivity to perturbations or its contribution to the overall complexityof the model. Different pruning algorithms may havedifferent criteria and processes, but the overall goal remains the same – to reduce the size of the network while maintaining its performance.中文回答：神经网络传统剪枝算法是一种通过移除不必要的连接或神经元来减小神经网络大小的技术。

人工智能领域中英文专有名词汇总

名词解释中英文对比<using_information_sources> social networks 社会网络abductive reasoning 溯因推理action recognition(行为识别)active learning(主动学习)adaptive systems 自适应系统adverse drugs reactions(药物不良反应)algorithm design and analysis(算法设计与分析) algorithm(算法)artificial intelligence 人工智能association rule(关联规则)attribute value taxonomy 属性分类规范automomous agent 自动代理automomous systems 自动系统background knowledge 背景知识bayes methods(贝叶斯方法)bayesian inference(贝叶斯推断)bayesian methods(bayes 方法)belief propagation(置信传播)better understanding 内涵理解big data 大数据big data(大数据)biological network(生物网络)biological sciences(生物科学)biomedical domain 生物医学领域biomedical research(生物医学研究)biomedical text(生物医学文本)boltzmann machine(玻尔兹曼机)bootstrapping method 拔靴法case based reasoning 实例推理causual models 因果模型citation matching (引文匹配)classification (分类)classification algorithms(分类算法)clistering algorithms 聚类算法cloud computing(云计算)cluster-based retrieval (聚类检索)clustering (聚类)clustering algorithms(聚类算法)clustering 聚类cognitive science 认知科学collaborative filtering (协同过滤)collaborative filtering(协同过滤)collabrative ontology development 联合本体开发collabrative ontology engineering 联合本体工程commonsense knowledge 常识communication networks(通讯网络)community detection(社区发现)complex data(复杂数据)complex dynamical networks(复杂动态网络)complex network(复杂网络)complex network(复杂网络)computational biology 计算生物学computational biology(计算生物学)computational complexity(计算复杂性) computational intelligence 智能计算computational modeling(计算模型)computer animation(计算机动画)computer networks(计算机网络)computer science 计算机科学concept clustering 概念聚类concept formation 概念形成concept learning 概念学习concept map 概念图concept model 概念模型concept modelling 概念模型conceptual model 概念模型conditional random field(条件随机场模型) conjunctive quries 合取查询constrained least squares (约束最小二乘) convex programming(凸规划)convolutional neural networks(卷积神经网络) customer relationship management(客户关系管理) data analysis(数据分析)data analysis(数据分析)data center(数据中心)data clustering (数据聚类)data compression(数据压缩)data envelopment analysis (数据包络分析)data fusion 数据融合data generation(数据生成)data handling(数据处理)data hierarchy (数据层次)data integration(数据整合)data integrity 数据完整性data intensive computing(数据密集型计算)data management 数据管理data management(数据管理)data management(数据管理)data miningdata mining 数据挖掘data model 数据模型data models(数据模型)data partitioning 数据划分data point(数据点)data privacy(数据隐私)data security(数据安全)data stream(数据流)data streams(数据流)data structure( 数据结构)data structure(数据结构)data visualisation(数据可视化)data visualization 数据可视化data visualization(数据可视化)data warehouse(数据仓库)data warehouses(数据仓库)data warehousing(数据仓库)database management systems(数据库管理系统)database management(数据库管理)date interlinking 日期互联date linking 日期链接Decision analysis(决策分析)decision maker 决策者decision making (决策)decision models 决策模型decision models 决策模型decision rule 决策规则decision support system 决策支持系统decision support systems (决策支持系统) decision tree(决策树)decission tree 决策树deep belief network(深度信念网络)deep learning(深度学习)defult reasoning 默认推理density estimation(密度估计)design methodology 设计方法论dimension reduction(降维) dimensionality reduction(降维)directed graph(有向图)disaster management 灾害管理disastrous event(灾难性事件)discovery(知识发现)dissimilarity (相异性)distributed databases 分布式数据库distributed databases(分布式数据库) distributed query 分布式查询document clustering (文档聚类)domain experts 领域专家domain knowledge 领域知识domain specific language 领域专用语言dynamic databases(动态数据库)dynamic logic 动态逻辑dynamic network(动态网络)dynamic system(动态系统)earth mover's distance(EMD 距离) education 教育efficient algorithm(有效算法)electric commerce 电子商务electronic health records(电子健康档案) entity disambiguation 实体消歧entity recognition 实体识别entity recognition(实体识别)entity resolution 实体解析event detection 事件检测event detection(事件检测)event extraction 事件抽取event identificaton 事件识别exhaustive indexing 完整索引expert system 专家系统expert systems(专家系统)explanation based learning 解释学习factor graph(因子图)feature extraction 特征提取feature extraction(特征提取)feature extraction(特征提取)feature selection (特征选择)feature selection 特征选择feature selection(特征选择)feature space 特征空间first order logic 一阶逻辑formal logic 形式逻辑formal meaning prepresentation 形式意义表示formal semantics 形式语义formal specification 形式描述frame based system 框为本的系统frequent itemsets(频繁项目集)frequent pattern(频繁模式)fuzzy clustering (模糊聚类)fuzzy clustering (模糊聚类)fuzzy clustering (模糊聚类)fuzzy data mining(模糊数据挖掘)fuzzy logic 模糊逻辑fuzzy set theory(模糊集合论)fuzzy set(模糊集)fuzzy sets 模糊集合fuzzy systems 模糊系统gaussian processes(高斯过程)gene expression data 基因表达数据gene expression(基因表达)generative model(生成模型)generative model(生成模型)genetic algorithm 遗传算法genome wide association study(全基因组关联分析) graph classification(图分类)graph classification(图分类)graph clustering(图聚类)graph data(图数据)graph data(图形数据)graph database 图数据库graph database(图数据库)graph mining(图挖掘)graph mining(图挖掘)graph partitioning 图划分graph query 图查询graph structure(图结构)graph theory(图论)graph theory(图论)graph theory(图论)graph theroy 图论graph visualization(图形可视化)graphical user interface 图形用户界面graphical user interfaces(图形用户界面)health care 卫生保健health care(卫生保健)heterogeneous data source 异构数据源heterogeneous data(异构数据)heterogeneous database 异构数据库heterogeneous information network(异构信息网络) heterogeneous network(异构网络)heterogenous ontology 异构本体heuristic rule 启发式规则hidden markov model(隐马尔可夫模型)hidden markov model(隐马尔可夫模型)hidden markov models(隐马尔可夫模型) hierarchical clustering (层次聚类) homogeneous network(同构网络)human centered computing 人机交互技术human computer interaction 人机交互human interaction 人机交互human robot interaction 人机交互image classification(图像分类)image clustering (图像聚类)image mining( 图像挖掘)image reconstruction(图像重建)image retrieval (图像检索)image segmentation(图像分割)inconsistent ontology 本体不一致incremental learning(增量学习)inductive learning (归纳学习)inference mechanisms 推理机制inference mechanisms(推理机制)inference rule 推理规则information cascades(信息追随)information diffusion(信息扩散)information extraction 信息提取information filtering(信息过滤)information filtering(信息过滤)information integration(信息集成)information network analysis(信息网络分析) information network mining(信息网络挖掘) information network(信息网络)information processing 信息处理information processing 信息处理information resource management (信息资源管理) information retrieval models(信息检索模型) information retrieval 信息检索information retrieval(信息检索)information retrieval(信息检索)information science 情报科学information sources 信息源information system( 信息系统)information system(信息系统)information technology(信息技术)information visualization(信息可视化)instance matching 实例匹配intelligent assistant 智能辅助intelligent systems 智能系统interaction network(交互网络)interactive visualization(交互式可视化)kernel function(核函数)kernel operator (核算子)keyword search(关键字检索)knowledege reuse 知识再利用knowledgeknowledgeknowledge acquisitionknowledge base 知识库knowledge based system 知识系统knowledge building 知识建构knowledge capture 知识获取knowledge construction 知识建构knowledge discovery(知识发现)knowledge extraction 知识提取knowledge fusion 知识融合knowledge integrationknowledge management systems 知识管理系统knowledge management 知识管理knowledge management(知识管理)knowledge model 知识模型knowledge reasoningknowledge representationknowledge representation(知识表达) knowledge sharing 知识共享knowledge storageknowledge technology 知识技术knowledge verification 知识验证language model(语言模型)language modeling approach(语言模型方法) large graph(大图)large graph(大图)learning(无监督学习)life science 生命科学linear programming(线性规划)link analysis (链接分析)link prediction(链接预测)link prediction(链接预测)link prediction(链接预测)linked data(关联数据)location based service(基于位置的服务) loclation based services(基于位置的服务) logic programming 逻辑编程logical implication 逻辑蕴涵logistic regression(logistic 回归)machine learning 机器学习machine translation(机器翻译)management system(管理系统)management( 知识管理)manifold learning(流形学习)markov chains 马尔可夫链markov processes(马尔可夫过程)matching function 匹配函数matrix decomposition(矩阵分解)matrix decomposition(矩阵分解)maximum likelihood estimation(最大似然估计)medical research(医学研究)mixture of gaussians(混合高斯模型)mobile computing(移动计算)multi agnet systems 多智能体系统multiagent systems 多智能体系统multimedia 多媒体natural language processing 自然语言处理natural language processing(自然语言处理) nearest neighbor (近邻)network analysis( 网络分析)network analysis(网络分析)network analysis(网络分析)network formation(组网)network structure(网络结构)network theory(网络理论)network topology(网络拓扑)network visualization(网络可视化)neural network(神经网络)neural networks (神经网络)neural networks(神经网络)nonlinear dynamics(非线性动力学)nonmonotonic reasoning 非单调推理nonnegative matrix factorization (非负矩阵分解) nonnegative matrix factorization(非负矩阵分解) object detection(目标检测)object oriented 面向对象object recognition(目标识别)object recognition(目标识别)online community(网络社区)online social network(在线社交网络)online social networks(在线社交网络)ontology alignment 本体映射ontology development 本体开发ontology engineering 本体工程ontology evolution 本体演化ontology extraction 本体抽取ontology interoperablity 互用性本体ontology language 本体语言ontology mapping 本体映射ontology matching 本体匹配ontology versioning 本体版本ontology 本体论open government data 政府公开数据opinion analysis(舆情分析)opinion mining(意见挖掘)opinion mining(意见挖掘)outlier detection(孤立点检测)parallel processing(并行处理)patient care(病人医疗护理)pattern classification(模式分类)pattern matching(模式匹配)pattern mining(模式挖掘)pattern recognition 模式识别pattern recognition(模式识别)pattern recognition(模式识别)personal data(个人数据)prediction algorithms(预测算法)predictive model 预测模型predictive models(预测模型)privacy preservation(隐私保护)probabilistic logic(概率逻辑)probabilistic logic(概率逻辑)probabilistic model(概率模型)probabilistic model(概率模型)probability distribution(概率分布)probability distribution(概率分布)project management(项目管理)pruning technique(修剪技术)quality management 质量管理query expansion(查询扩展)query language 查询语言query language(查询语言)query processing(查询处理)query rewrite 查询重写question answering system 问答系统random forest(随机森林)random graph(随机图)random processes(随机过程)random walk(随机游走)range query(范围查询)RDF database 资源描述框架数据库RDF query 资源描述框架查询RDF repository 资源描述框架存储库RDF storge 资源描述框架存储real time(实时)recommender system(推荐系统)recommender system(推荐系统)recommender systems 推荐系统recommender systems(推荐系统)record linkage 记录链接recurrent neural network(递归神经网络) regression(回归)reinforcement learning 强化学习reinforcement learning(强化学习)relation extraction 关系抽取relational database 关系数据库relational learning 关系学习relevance feedback (相关反馈)resource description framework 资源描述框架restricted boltzmann machines(受限玻尔兹曼机) retrieval models(检索模型)rough set theroy 粗糙集理论rough set 粗糙集rule based system 基于规则系统rule based 基于规则rule induction (规则归纳)rule learning (规则学习)rule learning 规则学习schema mapping 模式映射schema matching 模式匹配scientific domain 科学域search problems(搜索问题)semantic (web) technology 语义技术semantic analysis 语义分析semantic annotation 语义标注semantic computing 语义计算semantic integration 语义集成semantic interpretation 语义解释semantic model 语义模型semantic network 语义网络semantic relatedness 语义相关性semantic relation learning 语义关系学习semantic search 语义检索semantic similarity 语义相似度semantic similarity(语义相似度)semantic web rule language 语义网规则语言semantic web 语义网semantic web(语义网)semantic workflow 语义工作流semi supervised learning(半监督学习)sensor data(传感器数据)sensor networks(传感器网络)sentiment analysis(情感分析)sentiment analysis(情感分析)sequential pattern(序列模式)service oriented architecture 面向服务的体系结构shortest path(最短路径)similar kernel function(相似核函数)similarity measure(相似性度量)similarity relationship (相似关系)similarity search(相似搜索)similarity(相似性)situation aware 情境感知social behavior(社交行为)social influence(社会影响)social interaction(社交互动)social interaction(社交互动)social learning(社会学习)social life networks(社交生活网络)social machine 社交机器social media(社交媒体)social media(社交媒体)social media(社交媒体)social network analysis 社会网络分析social network analysis(社交网络分析)social network(社交网络)social network(社交网络)social science(社会科学)social tagging system(社交标签系统)social tagging(社交标签)social web(社交网页)sparse coding(稀疏编码)sparse matrices(稀疏矩阵)sparse representation(稀疏表示)spatial database(空间数据库)spatial reasoning 空间推理statistical analysis(统计分析)statistical model 统计模型string matching(串匹配)structural risk minimization (结构风险最小化) structured data 结构化数据subgraph matching 子图匹配subspace clustering(子空间聚类)supervised learning( 有support vector machine 支持向量机support vector machines(支持向量机)system dynamics(系统动力学)tag recommendation(标签推荐)taxonmy induction 感应规范temporal logic 时态逻辑temporal reasoning 时序推理text analysis(文本分析)text anaylsis 文本分析text classification (文本分类)text data(文本数据)text mining technique(文本挖掘技术)text mining 文本挖掘text mining(文本挖掘)text summarization(文本摘要)thesaurus alignment 同义对齐time frequency analysis(时频分析)time series analysis( 时time series data(时间序列数据)time series data(时间序列数据)time series(时间序列)topic model(主题模型)topic modeling(主题模型)transfer learning 迁移学习triple store 三元组存储uncertainty reasoning 不精确推理undirected graph(无向图)unified modeling language 统一建模语言unsupervisedupper bound(上界)user behavior(用户行为)user generated content(用户生成内容)utility mining(效用挖掘)visual analytics(可视化分析)visual content(视觉内容)visual representation(视觉表征)visualisation(可视化)visualization technique(可视化技术) visualization tool(可视化工具)web 2.0(网络2.0)web forum(web 论坛)web mining(网络挖掘)web of data 数据网web ontology lanuage 网络本体语言web pages(web 页面)web resource 网络资源web science 万维科学web search (网络检索)web usage mining(web 使用挖掘)wireless networks 无线网络world knowledge 世界知识world wide web 万维网world wide web(万维网)xml database 可扩展标志语言数据库附录 2 Data Mining 知识图谱（共包含二级节点15 个，三级节点93 个）间序列分析)监督学习)领域二级分类三级分类。

文献 (10)Semi-supervised and unsupervised extreme learning

Semi-supervised and unsupervised extreme learningmachinesGao Huang,Shiji Song,Jatinder N.D.Gupta,and Cheng WuAbstract—Extreme learning machines(ELMs)have proven to be an efﬁcient and effective learning paradigm for pattern classiﬁcation and regression.However,ELMs are primarily applied to supervised learning problems.Only a few existing research studies have used ELMs to explore unlabeled data. In this paper,we extend ELMs for both semi-supervised and unsupervised tasks based on the manifold regularization,thus greatly expanding the applicability of ELMs.The key advantages of the proposed algorithms are1)both the semi-supervised ELM (SS-ELM)and the unsupervised ELM(US-ELM)exhibit the learning capability and computational efﬁciency of ELMs;2) both algorithms naturally handle multi-class classiﬁcation or multi-cluster clustering;and3)both algorithms are inductive and can handle unseen data at test time directly.Moreover,it is shown in this paper that all the supervised,semi-supervised and unsupervised ELMs can actually be put into a uniﬁed framework. This provides new perspectives for understanding the mechanism of random feature mapping,which is the key concept in ELM theory.Empirical study on a wide range of data sets demonstrates that the proposed algorithms are competitive with state-of-the-art semi-supervised or unsupervised learning algorithms in terms of accuracy and efﬁciency.Index Terms—Clustering,embedding,extreme learning ma-chine,manifold regularization,semi-supervised learning,unsu-pervised learning.I.I NTRODUCTIONS INGLE layer feedforward networks(SLFNs)have been intensively studied during the past several decades.Most of the existing learning algorithms for training SLFNs,such as the famous back-propagation algorithm[1]and the Levenberg-Marquardt algorithm[2],adopt gradient methods to optimize the weights in the network.Some existing works also use forward selection or backward elimination approaches to con-struct network dynamically during the training process[3]–[7].However,neither the gradient based methods nor the grow/prune methods guarantee a global optimal solution.Al-though various methods,such as the generic and evolutionary algorithms,have been proposed to handle the local minimum This work was supported by the National Natural Science Foundation of China under Grant61273233,the Research Fund for the Doctoral Program of Higher Education under Grant20120002110035and20130002130010, the National Key Technology R&D Program under Grant2012BAF01B03, the Project of China Ocean Association under Grant DY125-25-02,and Tsinghua University Initiative Scientiﬁc Research Program under Grants 2011THZ07132.Gao Huang,Shiji Song,and Cheng Wu are with the Department of Automation,Tsinghua University,Beijing100084,China(e-mail:huang-g09@;shijis@; wuc@).Jatinder N.D.Gupta is with the College of Business Administration,The University of Alabama in Huntsville,Huntsville,AL35899,USA.(e-mail: guptaj@).problem,they basically introduce high computational cost. One of the most successful algorithms for training SLFNs is the support vector machines(SVMs)[8],[9],which is a maximal margin classiﬁer derived under the framework of structural risk minimization(SRM).The dual problem of SVMs is a quadratic programming and can be solved conveniently.Due to its simplicity and stable generalization performance,SVMs have been widely studied and applied to various domains[10]–[14].Recently,Huang et al.[15],[16]proposed the extreme learning machines(ELMs)for training SLFNs.In contrast to most of the existing approaches,ELMs only update the output weights between the hidden layer and the output layer, while the parameters,i.e.,the input weights and biases,of the hidden layer are randomly generated.By adopting squared loss on the prediction error,the training of output weights turns into a regularized least squares(or ridge regression)problem which can be solved efﬁciently in closed form.It has been shown that even without updating the parameters of the hidden layer,the SLFN with randomly generated hidden neurons and tunable output weights maintains its universal approximation capability[17]–[19].Compared to gradient based algorithms, ELMs are much more efﬁcient and usually lead to better generalization performance[20]–[22].Compared to SVMs, solving the regularized least squares problem in ELMs is also faster than solving the quadratic programming problem in standard SVMs.Moreover,ELMs can be used for multi-class classiﬁcation problems directly.The predicting accuracy achieved by ELMs is comparable with or even higher than that of SVMs[16],[22]–[24].The differences and similarities between ELMs and SVMs are discussed in[25]and[26], and new algorithms are proposed by combining the advan-tages of both models.In[25],an extreme SVM(ESVM) model is proposed by combining ELMs and the proximal SVM(PSVM).The ESVM algorithm is shown to be more accurate than the basic ELMs model due to the introduced regularization technique,and much more efﬁcient than SVMs since there is no kernel matrix multiplication in ESVM.In [26],the traditional RBF kernel are replaced by ELM kernel, leading to an efﬁcient algorithm with matched accuracy of SVMs.In the past years,researchers from variesﬁelds have made substantial contribution to ELM theories and applications.For example,the universal approximation ability of ELMs has been further studied in a classiﬁcation context[23].The gen-eralization error bound of ELMs has been investigated from the perspective of the Vapnik-Chervonenkis(VC)dimension theory and the initial localized generalization error model(LGEM)[27],[28].Varies extensions have been made to the basic ELMs to make it more efﬁcient and more suitable for speciﬁc problems,such as ELMs for online sequential data [29]–[31],ELMs for noisy/missing data[32]–[34],ELMs for imbalanced data[35],etc.From the implementation aspect, ELMs has recently been implemented using parallel tech-niques[36],[37],and realized on hardware[38],which made ELMs feasible for large data sets and real time reasoning. Though ELMs have become popular in a wide range of domains,they are primarily used for supervised learning tasks such as classiﬁcation and regression,which greatly limits their applicability.In some cases,such as text classiﬁcation, information retrieval and fault diagnosis,obtaining labels for fully supervised learning is time consuming and expensive, while a multitude of unlabeled data are easy and cheap to collect.To overcome the disadvantage of supervised learning al-gorithms that they cannot make use of unlabeled data,semi-supervised learning(SSL)has been proposed to leverage both labeled and unlabeled data[39],[40].The SSL algorithms assume that the input patterns from both labeled and unlabeled data are drawn from the same marginal distribution.Therefore, the unlabeled data naturally provide useful information for exploring the data structure in the input space.By assuming that the input data follows some cluster structure or manifold in the input space,SSL algorithms can incorporate both la-beled and unlabeled data into the learning process.Since SSL requires less effort to collect labeled data and can offer higher accuracy,it has been applied to various domains[41]–[43].In some other cases where no labeled data are available,people may be interested in exploring the underlying structure of the data.To this end,unsupervised learning(USL)techniques, such as clustering,dimension reduction or data representation, are widely used to fulﬁll these tasks.In this paper,we extend ELMs to handle both semi-supervised and unsupervised learning problems by introducing the manifold regularization framework.Both the proposed semi-supervised ELM(SS-ELM)and unsupervised ELM(US-ELM)inherit the computational efﬁciency and the learn-ing capability of traditional pared with existing algorithms,SS-ELM and US-ELM are not only inductive (straightforward extension for out-of-sample examples at test time),but also can be used for multi-class classiﬁcation or multi-cluster clustering directly.We test our algorithms on a variety of data sets,and make comparisons with other related algorithms.The results show that the proposed algorithms are competitive with state-of-the-art algorithms in terms of accuracy and efﬁciency.It is worth to mention that all the supervised,semi-supervised and unsupervised ELMs can actually be put into a uniﬁed framework,that is all the algorithms consist of two stages:1)random feature mapping;and2)output weights solving.Theﬁrst stage is to construct the hidden layer using randomly generated hidden neurons.This is the key concept in the ELM theory,which differs it from many existing feature learning methods.Generating feature mapping randomly en-ables ELMs for fast nonlinear feature learning and alleviates the problem of over-ﬁtting.The second stage is to solve the weights between the hidden layer and the output layer, and this is where the main difference of supervised,semi-supervised and unsupervised ELMs lies.We believe that the uniﬁed framework for the three types of ELMs might provide us a new perspective to understand the underlying behavior of the random feature mapping in ELMs.The rest of the paper is organized as follows.In Section II,we give a brief review of related existing literature on semi-supervised and unsupervised learning.Section III and IV introduce the basic formulation of ELMs and the man-ifold regularization framework,respectively.We present the proposed SS-ELM and US-ELM algorithms in Sections V and VI.Experiment results are given in Section VII,and Section VIII concludes the paper.II.R ELATED WORKSOnly a few existing research studies on ELMs have dealt with the problem of semi-supervised learning or unsupervised learning.In[44]and[45],the manifold regularization frame-work was introduce into the ELMs model to leverage both labeled and unlabeled data,thus extended ELMs for semi-supervised learning.However,both of these two works are limited to binary classiﬁcation problems,thus they haven’t explore the full power of ELMs.Moreover,both algorithms are only effective when the number of training patterns is more than the number of hidden neurons.Unfortunately,this condition is usually violated in semi-supervised learning since the training data is relatively scarce compared to the hidden neurons,whose number is commonly set to several hundreds or several thousands.Recently,a co-training approach have been proposed to train ELMs in a semi-supervised setting [46].In this algorithm,the labeled training sets are augmented gradually by moving a small set of most conﬁdently predicted unlabeled data to the labeled set at each loop,and ELMs are trained repeatedly on the pseudo-labeled set.Since the algo-rithm need to train ELMs repeatedly,it introduces considerable extra computational cost.The proposed SS-ELM is related to a few other mani-fold assumption based semi-supervised learning algorithms, such as the Laplacian support vector machines(LapSVMs) [47],the Laplacian regularized least squares(LapRLS)[47], semi-supervised neural networks(SSNNs)[48],and semi-supervised deep embedding[49].It has been shown in these works that manifold regularization is effective in a wide range of domains and often leads to a state-of-the-art performance in terms of accuracy and efﬁciency.The US-ELM proposed in this paper are related to the Laplacian Eigenmaps(LE)[50]and spectral clustering(SC) [51]in that they both use spectral techniques for embedding and clustering.In all these algorithms,an afﬁnity matrix is ﬁrst built from the input patterns.The SC performs eigen-decomposition on the normalized afﬁnity matrix,and then embeds the original data into a d-dimensional space using the ﬁrst d eigenvectors(each row is normalized to have unit length and represents a point in the embedded space)corresponding to the d largest eigenvalues.The LE algorithm performs generalized eigen-decomposition on the graph Laplacian,anduses the d eigenvectors corresponding to the second through the(d+1)th smallest eigenvalues for embedding.When LE and SC are used for clustering,then k-means is adopted to cluster the data in the embedded space.Similar to LE and SC,the US-ELM are also based on the afﬁnity matrix,and it is converted to solving a generalized eigen-decomposition problem.However,the eigenvectors obtained in US-ELM are not used for data representation directly,but are used as the parameters of the network,i.e.,the output weights.Note that once the US-ELM model is trained,it can be applied to any presented data in the original input space.In this way,US-ELM provide a straightforward way for handling new patterns without recomputing eigenvectors as in LE and SC.III.E XTREME LEARNING MACHINES Consider a supervised learning problem where we have a training set with N samples,{X,Y}={x i,y i}N i=1.Herex i∈R n i,y i is a n o-dimensional binary vector with only one entry(correspond to the class that x i belongs to)equal to one for multi-classiﬁcation tasks,or y i∈R n o for regression tasks,where n i and n o are the dimensions of input and output respectively.ELMs aim to learn a decision rule or an approximation function based on the training data. Generally,the training of ELMs consists of two stages.The ﬁrst stage is to construct the hidden layer using aﬁxed number of randomly generated mapping neurons,which can be any nonlinear piecewise continuous functions,such as the Sigmoid function and Gaussian function given below.1)Sigmoid functiong(x;θ)=11+exp(−(a T x+b));(1)2)Gaussian functiong(x;θ)=exp(−b∥x−a∥);(2) whereθ={a,b}are the parameters of the mapping function and∥·∥denotes the Euclidean norm.A notable feature of ELMs is that the parameters of the hidden mapping functions can be randomly generated ac-cording to any continuous probability distribution,e.g.,the uniform distribution on(-1,1).This makes ELMs distinct from the traditional feedforward neural networks and SVMs. The only free parameters that need to be optimized in the training process are the output weights between the hidden neurons and the output nodes.By doing so,training ELMs is equivalent to solving a regularized least squares problem which is considerately more efﬁcient than the training of SVMs or backpropagation algorithms.In theﬁrst stage,a number of hidden neurons which map the data from the input space into a n h-dimensional feature space (n h is the number of hidden neurons)are randomly generated. We denote by h(x i)∈R1×n h the output vector of the hidden layer with respect to x i,andβ∈R n h×n o the output weights that connect the hidden layer with the output layer.Then,the outputs of the network are given byf(x i)=h(x i)β,i=1,...,N.(3)In the second stage,ELMs aim to solve the output weights by minimizing the sum of the squared losses of the prediction errors,which leads to the following formulationminβ∈R n h×n o12∥β∥2+C2N∑i=1∥e i∥2s.t.h(x i)β=y T i−e T i,i=1,...,N,(4)where theﬁrst term in the objective function is a regularization term which controls the complexity of the model,e i∈R n o is the error vector with respect to the i th training pattern,and C is a penalty coefﬁcient on the training errors.By substituting the constraints into the objective function, we obtain the following equivalent unconstrained optimization problem:minβ∈R n h×n oL ELM=12∥β∥2+C2∥Y−Hβ∥2(5)where H=[h(x1)T,...,h(x N)T]T∈R N×n h.The above problem is widely known as the ridge regression or regularized least squares.By setting the gradient of L ELM with respect toβto zero,we have∇L ELM=β+CH H T(Y−Hβ)=0(6) If H has more rows than columns and is of full column rank,which is usually the case where the number of training patterns are more than the number of the hidden neurons,the above equation is overdetermined,and we have the following closed form solution for(5):β∗=(H T H+I nhC)−1H T Y,(7)where I nhis an identity matrix of dimension n h.Note that in practice,rather than explicitly inverting the n h×n h matrix in the above expression,we can use Gaussian elimination to directly solve a set of linear equations in a more efﬁcient and numerically stable manner.If the number of training patterns are less than the number of hidden neurons,then H will have more columns than rows, which often leads to an underdetermined least squares prob-lem.In this case,βmay have inﬁnite number of solutions.To handle this problem,we restrictβto be a linear combination of the rows of H:β=H Tα(α∈R N×n o).Notice that when H has more columns than rows and is of full row rank,then H H T is invertible.Multiplying both side of(6) by(H H T)−1H,we getα+C(Y−H H Tα)=0,(8) This yieldsβ∗=H Tα∗=H T(H H T+I NC)−1Y(9)where I N is an identity matrix of dimension N. Therefore,in the case where training patterns are plentiful compared to the hidden neurons,we use(7)to compute the output weights,otherwise we use(9).IV.T HE MANIFOLD REGULARIZATION FRAMEWORK Semi-supervised learning is built on the following two assumptions:(1)both the label data X l and the unlabeled data X u are drawn from the same marginal distribution P X ;and (2)if two points x 1and x 2are close to each other,then the conditional probabilities P (y |x 1)and P (y |x 2)should be similar as well.The latter assumption is widely known as the smoothness assumption in machine learning.To enforce this assumption on the data,the manifold regularization framework proposes to minimize the following cost functionL m=12∑i,jw ij ∥P (y |x i )−P (y |x j )∥2,(10)where w ij is the pair-wise similarity between two patterns x iand x j .Note that the similarity matrix W =[w ij ]is usually sparse,since we only place a nonzero weight between two patterns x i and x j if they are close,e.g.,x i is among the k nearest neighbors of x j or x j is among the k nearest neighbors of x i .The nonzero weights are usually computed using Gaussian function exp (−∥x i −x j ∥2/2σ2),or simply ﬁxed to 1.Intuitively,the formulation (10)penalizes large variation in the conditional probability P (y |x )when x has a small change.This requires that P (y |x )vary smoothly along the geodesics of P (x ).Since it is difﬁcult to compute the conditional probability,we can approximate (10)with the following expression:ˆLm =12∑i,jw ij ∥ˆyi −ˆy j ∥2,(11)where ˆyi and ˆy j are the predictions with respect to pattern x i and x j ,respectively.It is straightforward to simplify the above expression in a matrix form:ˆL m =Tr (ˆY T L ˆY ),(12)where Tr (·)denotes the trace of a matrix,L =D −W isknown as the graph Laplacian ,and D is a diagonal matrixwith its diagonal elements D ii =l +u∑j =1w i,j .As discussed in [52],instead of using L directly,we can normalize it byD −12L D −12or replace it by L p (p is an integer),based on some prior knowledge.V.S EMI -SUPERVISED ELMIn the semi-supervised setting,we have few labeled data and plenty of unlabeled data.We denote the labeled data in the training set as {X l ,Y l }={x i ,y i }l i =1,and unlabeled dataas X u ={x i }ui =1,where l and u are the number of labeled and unlabeled data,respectively.The proposed SS-ELM incorporates the manifold regular-ization to leverage unlabeled data to improve the classiﬁcation accuracy when labeled data are scarce.By modifying the ordinary ELM formulation (4),we give the formulation ofSS-ELM as:minβ∈R n h ×n o12∥β∥2+12l∑i =1C i ∥e i ∥2+λ2Tr (F T L F )s.t.h (x i )β=y T i −e T i ,i =1,...,l,f i =h (x i )β,i =1,...,l +u(13)where L ∈R (l +u )×(l +u )is the graph Laplacian built fromboth labeled and unlabeled data,and F ∈R (l +u )×n o is the output matrix of the network with its i th row equal to f (x i ),λis a tradeoff parameter.Note that similar to the weighted ELM algorithm (W-ELM)introduced in [35],here we associate different penalty coefﬁ-cient C i on the prediction errors with respect to patterns from different classes.This is because we found that when the data is skewed,i.e.,some classes have signiﬁcantly more training patterns than other classes,traditional ELMs tend to ﬁt the classes that having the majority of patterns quite well but ﬁts other classes poorly.This usually leads to poor generalization performance on the testing set (while the prediction accuracy may be high,but the some classes are neglected).Therefore,we propose to alleviate this problem by re-weighting instances from different classes.Suppose that x i belongs to class t i ,which has N t i training patterns,then we associate e i with a penalty ofC i =C 0N t i.(14)where C 0is a user deﬁned parameter as in traditional ELMs.In this way,the patterns from the dominant classes will not be over ﬁtted by the algorithm,and the patterns from a class with less samples will not be neglected.We substitute the constraints into the objective function,and rewrite the above formulation in a matrix form:min β∈R n h×n o 12∥β∥2+12∥C 12( Y −Hβ)∥2+λ2Tr (βT H TL Hβ)(15)where Y∈R (l +u )×n o is the training target with its ﬁrst l rows equal to Y l and the rest equal to 0,C is a (l +u )×(l +u )diagonal matrix with its ﬁrst l diagonal elements [C ]ii =C i ,i =1,...,l and the rest equal to 0.Again,we compute the gradient of the objective function with respect to β:∇L SS −ELM =β+H T C ( Y−H β)+λH H T L H β.(16)By setting the gradient to zero,we obtain the solution tothe SS-ELM:β∗=(I n h +H T C H +λH H T L H )−1H TC Y .(17)As in Section III,if the number of labeled data is fewer thanthe number of hidden neurons,which is common in SSL,we have the following alternative solution:β∗=H T (I l +u +C H H T +λL L H H T )−1C Y .(18)where I l +u is an identity matrix of dimension l +u .Note that by settingλto be zero and the diagonal elements of C i(i=1,...,l)to be the same constant,(17)and (18)reduce to the solutions of traditional ELMs(7)and(9), respectively.Based on the above discussion,the SS-ELM algorithm is summarized as Algorithm1.Algorithm1The SS-ELM algorithmInput:The labeled patterns,{X l,Y l}={x i,y i}l i=1;The unlabeled patterns,X u={x i}u i=1;Output:The mapping function of SS-ELM:f:R n i→R n oStep1:Construct the graph Laplacian L from both X l and X u.Step2:Initiate an ELM network of n h hidden neurons with random input weights and biases,and calculate the output matrix of the hidden neurons H∈R(l+u)×n h.Step3:Choose the tradeoff parameter C0andλ.Step4:•If n h≤NCompute the output weightsβusing(17)•ElseCompute the output weightsβusing(18)return The mapping function f(x)=h(x)β.VI.U NSUPERVISED ELMIn this section,we introduce the US-ELM algorithm for unsupervised learning.In an unsupervised setting,the entire training data X={x i}N i=1are unlabeled(N is the number of training patterns)and our target is toﬁnd the underlying structure of the original data.The formulation of US-ELM follows from the formulation of SS-ELM.When there is no labeled data,(15)is reduced tomin β∈R n h×n o ∥β∥2+λTr(βT H T L Hβ)(19)Notice that the above formulation always attains its mini-mum atβ=0.As suggested in[50],we have to introduce addtional constraints to avoid a degenerated solution.Speciﬁ-cally,the formulation of US-ELM is given bymin β∈R n h×n o ∥β∥2+λTr(βT H T L Hβ)s.t.(Hβ)T Hβ=I no(20)Theorem1:An optimal solution to problem(20)is given by choosingβas the matrix whose columns are the eigenvectors (normalized to satisfy the constraint)corresponding to theﬁrst n o smallest eigenvalues of the generalized eigenvalue problem:(I nh +λH H T L H)v=γH H T H v.(21)Proof:We can rewrite the problem(20)asminβ∈R n h×n o,ββT Bβ=I no Tr(βT Aβ),(22)Algorithm2The US-ELM algorithmInput:The training data:X∈R N×n i;Output:•For embedding task:The embedding in a n o-dimensional space:E∈R N×n o;•For clustering task:The label vector of cluster index:y∈N N×1+.Step1:Construct the graph Laplacian L from X.Step2:Initiate an ELM network of n h hidden neurons withrandom input weights,and calculate the output matrix of thehidden neurons H∈R N×n h.Step3:•If n h≤NFind the generalized eigenvectors v2,v3,...,v no+1of(21)corresponding to the second through the n o+1smallest eigenvalues.Letβ=[ v2, v3,..., v no+1],where v i=v i/∥H v i∥,i=2,...,n o+1.•ElseFind the generalized eigenvectors u2,u3,...,u no+1of(24)corresponding to the second through the n o+1smallest eigenvalues.Letβ=H T[ u2, u3,..., u no+1],where u i=u i/∥H H T u i∥,i=2,...,n o+1.Step4:Calculate the embedding matrix:E=Hβ.Step5(For clustering only):Treat each row of E as a point,and cluster the N points into K clusters using the k-meansalgorithm.Let y be the label vector of cluster index for allthe points.return E(for embedding task)or y(for clustering task);where A=I nh+λH H T L H and B=H T H.It is easy to verify that both A and B are Hermitianmatrices.Thus,according to the Rayleigh-Ritz theorem[53],the above trace minimization problem attains its optimum ifand only if the column span ofβis the minimum span ofthe eigenspace corresponding to the smallest n o eigenvaluesof(21).Therefore,by stacking the normalized eigenvectors of(21)corresponding to the smallest n o generalized eigenvalues,we obtain an optimal solution to(20).In the algorithm of Laplacian eigenmaps,theﬁrst eigenvec-tor is discarded since it is always a constant vector proportionalto1(corresponding to the smallest eigenvalue0)[50].In theUS-ELM algorithm,theﬁrst eigenvector of(21)also leadsto small variations in embedding and is not useful for datarepresentation.Therefore,we suggest to discard this trivialsolution as well.Letγ1,γ2,...,γno+1(γ1≤γ2≤...≤γn o+1)be the(n o+1)smallest eigenvalues of(21)and v1,v2,...,v no+1be their corresponding eigenvectors.Then,the solution to theoutput weightsβis given byβ∗=[ v2, v3,..., v no+1],(23)where v i=v i/∥H v i∥,i=2,...,n o+1are the normalizedeigenvectors.If the number of labeled data is fewer than the numberTABLE ID ETAILS OF THE DATA SETS USED FOR SEMI-SUPERVISED LEARNINGData set Class Dimension|L||U||V||T|G50C2505031450136COIL20(B)2102440100040360USPST(B)225650140950498COIL2020102440100040360USPST1025650140950498of hidden neurons,problem(21)is underdetermined.In this case,we have the following alternative formulation by using the same trick as in previous sections:(I u+λL L H H T )u=γH H H T u.(24)Again,let u1,u2,...,u no +1be generalized eigenvectorscorresponding to the(n o+1)smallest eigenvalues of(24), then theﬁnal solution is given byβ∗=H T[ u2, u3,..., u no +1],(25)where u i=u i/∥H H T u i∥,i=2,...,n o+1are the normal-ized eigenvectors.If our task is clustering,then we can adopt the k-means algorithm to perform clustering in the embedded space.We summarize the proposed US-ELM in Algorithm2. Remark:Comparing the supervised ELM,the semi-supervised ELM and the unsupervised ELM,we can observe that all the algorithms have two similar stages in the training process,that is the random feature learning stage and the out-put weights learning stage.Under this two-stage framework,it is easy toﬁnd the differences and similarities between the three algorithms.Actually,all the algorithms share the same stage of random feature learning,and this is the essence of the ELM theory.This also means that no matter the task is a supervised, semi-supervised or unsupervised learning problem,we can always follow the same step to generate the hidden layer. The differences of the three types of ELMs lie in the second stage on how the output weights are computed.In supervised ELM and SS-ELM,the output weights are trained by solving a regularized least squares problem;while the output weights in the US-ELM are obtained by solving a generalized eigenvalue problem.The uniﬁed framework for the three types of ELMs might provide new perspectives to further develop the ELM theory.VII.E XPERIMENTAL RESULTSWe evaluated our algorithms on wide range of semi-supervised and unsupervised parisons were made with related state-of-the-art algorithms, e.g.,Transductive SVM(TSVM)[54],LapSVM[47]and LapRLS[47]for semi-supervised learning;and Laplacian Eigenmap(LE)[50], spectral clustering(SC)[51]and deep autoencoder(DA)[55] for unsupervised learning.All algorithms were implemented using Matlab R2012a on a2.60GHz machine with4GB of memory.TABLE IIIT RAINING TIME(IN SECONDS)COMPARISON OF TSVM,L AP RLS,L AP SVM AND SS-ELMData set TSVM LapRLS LapSVM SS-ELMG50C0.3240.0410.0450.035COIL20(B)16.820.5120.4590.516USPST(B)68.440.9210.947 1.029COIL2018.43 5.841 4.9460.814USPST68.147.1217.259 1.373A.Semi-supervised learning results1)Data sets:We tested the SS-ELM onﬁve popular semi-supervised learning benchmarks,which have been widely usedfor evaluating semi-supervised algorithms[52],[56],[57].•The G50C is a binary classiﬁcation data set of which each class is generated by a50-dimensional multivariate Gaus-sian distribution.This classiﬁcation problem is explicitlydesigned so that the true Bayes error is5%.•The Columbia Object Image Library(COIL20)is a multi-class image classiﬁcation data set which consists1440 gray-scale images of20objects.Each pattern is a32×32 gray scale image of one object taken from a speciﬁc view.The COIL20(B)data set is a binary classiﬁcation taskobtained from COIL20by grouping theﬁrst10objectsas Class1,and the last10objects as Class2.•The USPST data set is a subset(the testing set)of the well known handwritten digit recognition data set USPS.The USPST(B)data set is a binary classiﬁcation task obtained from USPST by grouping theﬁrst5digits as Class1and the last5digits as Class2.2)Experimental setup:We followed the experimental setup in[57]to evaluate the semi-supervised algorithms.Speciﬁ-cally,each of the data sets is split into4folds,one of which was used for testing(denoted by T)and the rest3folds for training.Each of the folds was used as the testing set once(4-fold cross-validation).As in[57],this random fold generation process were repeated3times,resulted in12different splits in total.Every training set was further partitioned into a labeled set L,a validation set V,and an unlabeled set U.When we train a semi-supervised learning algorithm,the labeled data from L and the unlabeled data from U were used.The validation set which consists of labeled data was only used for model selection,i.e.,ﬁnding the optimal hyperparameters C0andλin the SS-ELM algorithm.The characteristics of the data sets used in our experiment are summarized in Table I. The training of SS-ELM consists of two stages:1)generat-ing the random hidden layer;and2)training the output weights using(17)or(18).In theﬁrst stage,we adopted the Sigmoid function for nonlinear mapping,and the input weights and biases were generated according to the uniform distribution on(-1,1).The number of hidden neurons n h wasﬁxed to 1000for G50C,and2000for the rest four data sets.In the second stage,weﬁrst need to build the graph Laplacian L.We followed the methods discussed in[52]and[57]to compute L,and the hyperparameter settings can be found in[47],[52] and[57].The trade off parameters C andλwere selected from。

集成学习综述

集成学习综述梁英毅摘要机器学习方法在生产、科研和生活中有着广泛应用，而集成学习则是机器学习的首要热门方向[1]。

集成学习是使用一系列学习器进行学习，并使用某种规则把各个学习结果进行整合从而获得比单个学习器更好的学习效果的一种机器学习方法。

本文对集成学习的概念以及一些主要的集成学习方法进行简介，以便于进行进一步的研究。

一、引言机器学习是计算机科学中研究怎么让机器具有学习能力的分支，[2]把机器学习的目标归纳为“给出关于如何进行学习的严格的、计算上具体的、合理的说明”。

[3]指出四类问题的解决对于人类来说是困难的甚至不可能的，从而说明机器学习的必要性。

目前，机器学习方法已经在科学研究、语音识别、人脸识别、手写识别、数据挖掘、医疗诊断、游戏等等领域之中得到应用[1, 4]。

随着机器学习方法的普及，机器学习方面的研究也越来越热门，目前来说机器学习的研究主要分为四个大方向[1]： a) 通过集成学习方法提高学习精度；b) 扩大学习规模；c) 强化学习；d) 学习复杂的随机模型；有关Machine Learning 的进一步介绍请参考[5, 1,3, 4, 6]。

本文的目的是对集成学习的各种方法进行综述，以了解当前集成学习方面的进展和问题。

本文以下内容组织如下：第二节首先介绍集成学习；第三节对一些常见的集成学习方法进行简单介绍；第四节给出一些关于集成学习的分析方法和分析结果。

二、集成学习简介1、分类问题分类问题属于概念学习的范畴。

分类问题是集成学习的基本研究问题，简单来说就是把一系列实例根据某种规则进行分类，这实际上是要寻找某个函数)(x f y =，使得对于一个给定的实例x ，找出正确的分类。

机器学习中的解决思路是通过某种学习方法在假设空间中找出一个足够好的函数来近似，这个近似函数就叫做分类器[7]。

y h f h2、什么是集成学习传统的机器学习方法是在一个由各种可能的函数构成的空间（称为“假设空间”）中寻找一个最接近实际分类函数的分类器h [6]。

RcppEnsmallen 0.2.21.0.1 头文件 C++ 数学优化库的说明说明书

Package‘RcppEnsmallen’November27,2023Title Header-Only C++Mathematical Optimization Library for'Armadillo'Version0.2.21.0.1Description'Ensmallen'is a templated C++mathematical optimization library (by the'MLPACK'team)that provides a simple set of abstractions for writing anobjective function to optimize.Provided within are various standard andcutting-edge optimizers that include full-batch gradient descent techniques,small-batch techniques,gradient-free optimizers,and constrained optimization.The'RcppEnsmallen'package includes the headerﬁles from the'Ensmallen'library and pairs the appropriate headerﬁles from'armadillo'through the'RcppArmadillo'package.Therefore,users do not need to install'Ensmallen'nor'Armadillo'to use'RcppEnsmallen'.Note that'Ensmallen'is licensed under3-Clause BSD,'Armadillo'starting from7.800.0is licensed under Apache License2, 'RcppArmadillo'(the'Rcpp'bindings/bridge to'Armadillo')is licensed underthe GNU GPL version2or later.Thus,'RcppEnsmallen'is also licensed undersimilar terms.Note that'Ensmallen'requires a compiler that supports'C++11'and'Armadillo'9.800or later.Depends R(>=4.0.0)License GPL(>=2)URL https:///coatless-rpkg/rcppensmallen,https:///rcppensmallen/,https:///mlpack/ensmallen,https:/// BugReports https:///coatless-rpkg/rcppensmallen/issues Encoding UTF-8LinkingTo Rcpp,RcppArmadillo(>=0.9.800.0.0)Imports RcppRoxygenNote7.2.3Suggests knitr,rmarkdownVignetteBuilder knitrNeedsCompilation yes12RcppEnsmallen-package Author James Joseph Balamuta[aut,cre,cph](<https:///0000-0003-2826-8458>),Dirk Eddelbuettel[aut,cph](<https:///0000-0001-6419-907X>)Maintainer James Joseph Balamuta<*********************>Repository CRANDate/Publication2023-11-2721:20:03UTCR topics documented:RcppEnsmallen-package (2)lin_reg_lbfgs (3)Index5 RcppEnsmallen-package RcppEnsmallen:Header-Only C++Mathematical Optimization Li-brary for’Armadillo’Description’Ensmallen’is a templated C++mathematical optimization library(by the’MLPACK’team)that provides a simple set of abstractions for writing an objective function to optimize.Provided within are various standard and cutting-edge optimizers that include full-batch gradient descent techniques, small-batch techniques,gradient-free optimizers,and constrained optimization.The’RcppEns-mallen’package includes the headerﬁles from the’Ensmallen’library and pairs the appropriate headerﬁles from’armadillo’through the’RcppArmadillo’package.Therefore,users do not need to install’Ensmallen’nor’Armadillo’to use’RcppEnsmallen’.Note that’Ensmallen’is licensed under3-Clause BSD,’Armadillo’starting from7.800.0is licensed under Apache License2,’Rcp-pArmadillo’(the’Rcpp’bindings/bridge to’Armadillo’)is licensed under the GNU GPL version2 or later.Thus,’RcppEnsmallen’is also licensed under similar terms.Note that’Ensmallen’requiresa compiler that supports’C++11’and’Armadillo’9.800or later.Author(s)Maintainer:James Joseph Balamuta<*********************>(ORCID)[copyright holder] Authors:•Dirk Eddelbuettel<**************>(ORCID)[copyright holder]See AlsoUseful links:•https:///coatless-rpkg/rcppensmallen•https:///rcppensmallen/•https:///mlpack/ensmallen•https:///•Report bugs at https:///coatless-rpkg/rcppensmallen/issues lin_reg_lbfgs Linear Regression with L-BFGSDescriptionSolves the Linear Regression’s Residual Sum of Squares using the L-BFGS optimizer. Usagelin_reg_lbfgs(X,y)ArgumentsX A matrix that is the Design Matrix for the regression problem.y A vec containing the response values.DetailsConsider the Residual Sum of Squares,also known as RSS,deﬁned as:RSS(β)=(y−Xβ)T(y−Xβ)The objective function is deﬁned as:f(β)=(y−Xβ)2The gradient is deﬁned as:∂RSS=−2X T(y−Xβ)∂βValueThe estimatedβparameter values for the linear regression.Examples#Number of Pointsn=1000#Select beta parametersbeta=c(-2,1.5,3,8.2,6.6)#Number of Predictors(including intercept)p=length(beta)#Generate predictors from a normal distributionX_i=matrix(rnorm(n),ncol=p-1)#Add an interceptX=cbind(1,X_i)#Generate y valuesy=X%*%beta+rnorm(n/(p-1))#Run optimization with lbfgstheta_hat=lin_reg_lbfgs(X,y)#Verify parameters were recoveredcbind(actual=beta,estimated=theta_hat)Indexlin_reg_lbfgs,3RcppEnsmallen(RcppEnsmallen-package),2 RcppEnsmallen-package,25。

ensrnop编号

ensrnop编号English Answer:ENSEMBL Transcript ID (ENST), also known as ENSEMBL Transcript Identification Number or ENSRNO, is a unique identifier assigned to each transcript model in the ENSEMBL database. It is a stable and versioned identifier that remains the same across ENSEMBL releases, allowing users to track and reference specific transcript models over time.ENSTs are composed of two parts:1. ENSEMBL Gene ID (ENSG): A stable and versioned identifier assigned to each gene model in the ENSEMBL database.2. Transcript version number: A numeric version number that indicates the specific version of the transcript model.ENSTs are typically used for the following purposes:Identifying and referencing specific transcript models in scientific publications and databases.Tracking changes and updates to transcript models over time.Performing comparative analyses of transcript models across different species or tissues.Developing and validating gene expression assays and other molecular biology techniques.中文回答：ENSEMBL 转录序列号 (ENST)，也称为 ENSEMBL 转录序列识别号或 ENSRNO，是分配给 ENSEMBL 数据库中每个转录本模型的唯一标识符。

Finding community structure in networks using the eigenvectors of matrices

Finding community structure in networks using the eigenvectors of matrices
M. E. J. Newman
Department of Physics and Center for the Study of Complex Systems, University of Michigan, Ann Arbor, MI 48109–1040
We consider the problem of detecting communities or modules in networks, groups of vertices with a higher-than-average density of edges connecting them. Previous work indicates that a robust approach to this problem is the maximization of the beneﬁt function known as “modularity” over possible divisions of a network. Here we show that this maximization process can be written in terms of the eigenspectrum of a matrix we call the modularity matrix, which plays a role in community detection similar to that played by the graph Laplacian in graph partitioning calculations. This result leads us to a number of possible algorithms for detecting community structure, as well as several other results, including a spectral measure of bipartite structure in neteasure that identiﬁes those vertices that occupy central positions within the communities to which they belong. The algorithms and measures proposed are illustrated with applications to a variety of real-world complex networks.

model ensemble 方法

model ensemble 方法（最新版3篇）目录（篇1）1.模型集成方法的概述2.模型集成方法的分类3.模型集成方法的优缺点4.模型集成方法的应用实例正文（篇1）一、模型集成方法的概述模型集成方法是指将多个模型结合起来，以提高预测准确性或泛化性能的一种技术。

在机器学习领域，模型集成被广泛应用，以期通过结合多个模型的优点，达到更好的性能。

二、模型集成方法的分类模型集成方法主要分为以下几类：1.基于模型的集成基于模型的集成方法是指将多个基模型的预测结果进行结合，得到最终的预测结果。

常见的方法包括平均法、投票法等。

2.基于特征的集成基于特征的集成方法是指将多个基模型对应的特征进行组合，得到新的特征输入到另一个模型中进行预测。

常见的方法包括特征加权、特征选择等。

3.基于模型和特征的集成基于模型和特征的集成方法是指既对基模型进行组合，也对基模型的特征进行组合。

常见的方法包括 Stacking 等。

1.优点模型集成可以提高预测准确性，降低过拟合风险，提高模型的泛化能力。

同时，模型集成可以利用多个模型的互补性，提高对复杂数据的处理能力。

2.缺点模型集成会增加计算复杂度，可能会降低模型的训练速度。

另外，模型集成方法需要对多个基模型进行选择和组合，可能需要一定的专业知识和经验。

四、模型集成方法的应用实例模型集成方法在很多领域都有广泛应用，例如在图像识别、自然语言处理、推荐系统等领域。

目录（篇2）1.模型集成方法的定义和重要性2.模型集成方法的分类3.模型集成方法的优缺点4.模型集成方法的应用实例5.模型集成方法的发展前景正文（篇2）一、模型集成方法的定义和重要性模型集成方法是指将多个预测模型结合起来，以提高预测准确性的一种技术。

在数据挖掘、机器学习以及人工智能领域，模型集成方法具有重要的地位，它可以有效提升模型的预测性能，对于解决复杂问题具有重要意义。

模型集成方法主要分为以下几类：1.基于模型的集成方法：将多个模型的预测结果进行结合，如投票法、Stacking 等。

ensemble的异常检测方法原理介绍

Ensemble methods in异常检测通常指的是将多个不同的异常检测算法结合起来，以提高检测的准确性和鲁棒性。

这种方法基于这样一个观察：单一的异常检测算法可能对某些数据集或异常类型表现良好，但对其他数据集或异常类型则表现不佳。

通过结合多个算法，可以取长补短，提高整体性能。

Ensemble methods的原理可以概括为以下几个步骤：1. 选择基检测器：选择多个不同的异常检测算法作为基检测器。

这些基检测器可以是基于统计方法的（如3-sigma原则、Z-score）、基于邻近度的（如k-最近邻、局部异常因子）、基于机器学习的（如支持向量机、隔离森林）等。

2. 独立检测：分别使用每个基检测器对数据集进行异常检测。

每个检测器根据自己的算法独立地评估每个数据点的异常性，并生成异常分数或标签。

3. 集成决策：将基检测器的结果进行集成，以形成一个最终的异常检测结果。

集成策略可以有多种，例如：- 投票法：每个检测器的异常分数或标签被用作投票，多数检测器认为异常的数据点被标记为异常。

- 平均法：计算所有检测器异常分数的平均值，高于某个阈值的点被认为是异常。

- 堆叠法（Stacking）：使用一个元学习算法（如随机森林、梯度提升机）来学习如何最优地结合基检测器的结果。

4. 优化和调整：对集成方法进行优化和调整，以提高性能。

这可能包括选择最佳的基检测器组合、调整集成策略的参数、使用交叉验证等方法。

Ensemble methods的优势在于其能够结合不同算法的特点，提高异常检测的准确性和鲁棒性。

然而，这种方法也需要更多的计算资源，并且可能需要复杂的调参过程。

此外，集成方法的效果也取决于基检测器的选择和集成策略的设计。

Consensus and Cooperation in Networked Multi-Agent Systems

Consensus and Cooperation in Networked Multi-Agent SystemsAlgorithms that provide rapid agreement and teamwork between all participants allow effective task performance by self-organizing networked systems.By Reza Olfati-Saber,Member IEEE,J.Alex Fax,and Richard M.Murray,Fellow IEEEABSTRACT|This paper provides a theoretical framework for analysis of consensus algorithms for multi-agent networked systems with an emphasis on the role of directed information flow,robustness to changes in network topology due to link/node failures,time-delays,and performance guarantees. An overview of basic concepts of information consensus in networks and methods of convergence and performance analysis for the algorithms are provided.Our analysis frame-work is based on tools from matrix theory,algebraic graph theory,and control theory.We discuss the connections between consensus problems in networked dynamic systems and diverse applications including synchronization of coupled oscillators,flocking,formation control,fast consensus in small-world networks,Markov processes and gossip-based algo-rithms,load balancing in networks,rendezvous in space, distributed sensor fusion in sensor networks,and belief propagation.We establish direct connections between spectral and structural properties of complex networks and the speed of information diffusion of consensus algorithms.A brief introduction is provided on networked systems with nonlocal information flow that are considerably faster than distributed systems with lattice-type nearest neighbor interactions.Simu-lation results are presented that demonstrate the role of small-world effects on the speed of consensus algorithms and cooperative control of multivehicle formations.KEYWORDS|Consensus algorithms;cooperative control; flocking;graph Laplacians;information fusion;multi-agent systems;networked control systems;synchronization of cou-pled oscillators I.INTRODUCTIONConsensus problems have a long history in computer science and form the foundation of the field of distributed computing[1].Formal study of consensus problems in groups of experts originated in management science and statistics in1960s(see DeGroot[2]and references therein). The ideas of statistical consensus theory by DeGroot re-appeared two decades later in aggregation of information with uncertainty obtained from multiple sensors1[3]and medical experts[4].Distributed computation over networks has a tradition in systems and control theory starting with the pioneering work of Borkar and Varaiya[5]and Tsitsiklis[6]and Tsitsiklis,Bertsekas,and Athans[7]on asynchronous asymptotic agreement problem for distributed decision-making systems and parallel computing[8].In networks of agents(or dynamic systems),B con-sensus[means to reach an agreement regarding a certain quantity of interest that depends on the state of all agents.A B consensus algorithm[(or protocol)is an interaction rule that specifies the information exchange between an agent and all of its neighbors on the network.2 The theoretical framework for posing and solving consensus problems for networked dynamic systems was introduced by Olfati-Saber and Murray in[9]and[10] building on the earlier work of Fax and Murray[11],[12]. The study of the alignment problem involving reaching an agreement V without computing any objective functions V appeared in the work of Jadbabaie et al.[13].Further theoretical extensions of this work were presented in[14] and[15]with a look toward treatment of directed infor-mation flow in networks as shown in Fig.1(a).Manuscript received August8,2005;revised September7,2006.This work was supported in part by the Army Research Office(ARO)under Grant W911NF-04-1-0316. R.Olfati-Saber is with Dartmouth College,Thayer School of Engineering,Hanover,NH03755USA(e-mail:olfati@).J.A.Fax is with Northrop Grumman Corp.,Woodland Hills,CA91367USA(e-mail:alex.fax@).R.M.Murray is with the California Institute of Technology,Control and Dynamical Systems,Pasadena,CA91125USA(e-mail:murray@).Digital Object Identifier:10.1109/JPROC.2006.8872931This is known as sensor fusion and is an important application of modern consensus algorithms that will be discussed later.2The term B nearest neighbors[is more commonly used in physics than B neighbors[when applied to particle/spin interactions over a lattice (e.g.,Ising model).Vol.95,No.1,January2007|Proceedings of the IEEE2150018-9219/$25.00Ó2007IEEEThe common motivation behind the work in [5],[6],and [10]is the rich history of consensus protocols in com-puter science [1],whereas Jadbabaie et al.[13]attempted to provide a formal analysis of emergence of alignment in the simplified model of flocking by Vicsek et al.[16].The setup in [10]was originally created with the vision of de-signing agent-based amorphous computers [17],[18]for collaborative information processing in ter,[10]was used in development of flocking algorithms with guaranteed convergence and the capability to deal with obstacles and adversarial agents [19].Graph Laplacians and their spectral properties [20]–[23]are important graph-related matrices that play a crucial role in convergence analysis of consensus and alignment algo-rithms.Graph Laplacians are an important point of focus of this paper.It is worth mentioning that the second smallest eigenvalue of graph Laplacians called algebraic connectivity quantifies the speed of convergence of consensus algo-rithms.The notion of algebraic connectivity of graphs has appeared in a variety of other areas including low-density parity-check codes (LDPC)in information theory and com-munications [24],Ramanujan graphs [25]in number theory and quantum chaos,and combinatorial optimization prob-lems such as the max-cut problem [21].More recently,there has been a tremendous surge of interest V among researchers from various disciplines of engineering and science V in problems related to multia-gent networked systems with close ties to consensus prob-lems.This includes subjects such as consensus [26]–[32],collective behavior of flocks and swarms [19],[33]–[37],sensor fusion [38]–[40],random networks [41],[42],syn-chronization of coupled oscillators [42]–[46],algebraic connectivity 3of complex networks [47]–[49],asynchro-nous distributed algorithms [30],[50],formation control for multirobot systems [51]–[59],optimization-based co-operative control [60]–[63],dynamic graphs [64]–[67],complexity of coordinated tasks [68]–[71],and consensus-based belief propagation in Bayesian networks [72],[73].A detailed discussion of selected applications will be pre-sented shortly.In this paper,we focus on the work described in five key papers V namely,Jadbabaie,Lin,and Morse [13],Olfati-Saber and Murray [10],Fax and Murray [12],Moreau [14],and Ren and Beard [15]V that have been instrumental in paving the way for more recent advances in study of self-organizing networked systems ,or swarms .These networked systems are comprised of locally interacting mobile/static agents equipped with dedicated sensing,computing,and communication devices.As a result,we now have a better understanding of complex phenomena such as flocking [19],or design of novel information fusion algorithms for sensor networks that are robust to node and link failures [38],[72]–[76].Gossip-based algorithms such as the push-sum protocol [77]are important alternatives in computer science to Laplacian-based consensus algorithms in this paper.Markov processes establish an interesting connection between the information propagation speed in these two categories of algorithms proposed by computer scientists and control theorists [78].The contribution of this paper is to present a cohesive overview of the key results on theory and applications of consensus problems in networked systems in a unified framework.This includes basic notions in information consensus and control theoretic methods for convergence and performance analysis of consensus protocols that heavily rely on matrix theory and spectral graph theory.A byproduct of this framework is to demonstrate that seem-ingly different consensus algorithms in the literature [10],[12]–[15]are closely related.Applications of consensus problems in areas of interest to researchers in computer science,physics,biology,mathematics,robotics,and con-trol theory are discussed in this introduction.A.Consensus in NetworksThe interaction topology of a network of agents is rep-resented using a directed graph G ¼ðV ;E Þwith the set of nodes V ¼f 1;2;...;n g and edges E V ÂV .TheFig.1.Two equivalent forms of consensus algorithms:(a)a networkof integrator agents in which agent i receives the state x j of its neighbor,agent j ,if there is a link ði ;j Þconnecting the two nodes;and (b)the block diagram for a network of interconnecteddynamic systems all with identical transfer functions P ðs Þ¼1=s .The collective networked system has a diagonal transfer function and is a multiple-input multiple-output (MIMO)linear system.3To be defined in Section II-A.Olfati-Saber et al.:Consensus and Cooperation in Networked Multi-Agent Systems216Proceedings of the IEEE |Vol.95,No.1,January 2007neighbors of agent i are denoted by N i ¼f j 2V :ði ;j Þ2E g .According to [10],a simple consensus algorithm to reach an agreement regarding the state of n integrator agents with dynamics _x i ¼u i can be expressed as an n th-order linear system on a graph_x i ðt Þ¼X j 2N ix j ðt ÞÀx i ðt ÞÀÁþb i ðt Þ;x i ð0Þ¼z i2R ;b i ðt Þ¼0:(1)The collective dynamics of the group of agents following protocol (1)can be written as_x ¼ÀLx(2)where L ¼½l ij is the graph Laplacian of the network and itselements are defined as follows:l ij ¼À1;j 2N i j N i j ;j ¼i :&(3)Here,j N i j denotes the number of neighbors of node i (or out-degree of node i ).Fig.1shows two equivalent forms of the consensus algorithm in (1)and (2)for agents with a scalar state.The role of the input bias b in Fig.1(b)is defined later.According to the definition of graph Laplacian in (3),all row-sums of L are zero because of P j l ij ¼0.Therefore,L always has a zero eigenvalue 1¼0.This zero eigenvalues corresponds to the eigenvector 1¼ð1;...;1ÞT because 1belongs to the null-space of L ðL 1¼0Þ.In other words,an equilibrium of system (2)is a state in the form x Ã¼ð ;...; ÞT ¼ 1where all nodes agree.Based on ana-lytical tools from algebraic graph theory [23],we later show that x Ãis a unique equilibrium of (2)(up to a constant multiplicative factor)for connected graphs.One can show that for a connected network,the equilibrium x Ã¼ð ;...; ÞT is globally exponentially stable.Moreover,the consensus value is ¼1=n P i z i that is equal to the average of the initial values.This im-plies that irrespective of the initial value of the state of each agent,all agents reach an asymptotic consensus regarding the value of the function f ðz Þ¼1=n P i z i .While the calculation of f ðz Þis simple for small net-works,its implications for very large networks is more interesting.For example,if a network has n ¼106nodes and each node can only talk to log 10ðn Þ¼6neighbors,finding the average value of the initial conditions of the nodes is more complicated.The role of protocol (1)is to provide a systematic consensus mechanism in such a largenetwork to compute the average.There are a variety of functions that can be computed in a similar fashion using synchronous or asynchronous distributed algorithms (see [10],[28],[30],[73],and [76]).B.The f -Consensus Problem and Meaning of CooperationTo understand the role of cooperation in performing coordinated tasks,we need to distinguish between un-constrained and constrained consensus problems.An unconstrained consensus problem is simply the alignment problem in which it suffices that the state of all agents asymptotically be the same.In contrast,in distributed computation of a function f ðz Þ,the state of all agents has to asymptotically become equal to f ðz Þ,meaning that the consensus problem is constrained.We refer to this con-strained consensus problem as the f -consensus problem .Solving the f -consensus problem is a cooperative task and requires willing participation of all the agents.To demonstrate this fact,suppose a single agent decides not to cooperate with the rest of the agents and keep its state unchanged.Then,the overall task cannot be performed despite the fact that the rest of the agents reach an agree-ment.Furthermore,there could be scenarios in which multiple agents that form a coalition do not cooperate with the rest and removal of this coalition of agents and their links might render the network disconnected.In a dis-connected network,it is impossible for all nodes to reach an agreement (unless all nodes initially agree which is a trivial case).From the above discussion,cooperation can be infor-mally interpreted as B giving consent to providing one’s state and following a common protocol that serves the group objective.[One might think that solving the alignment problem is not a cooperative task.The justification is that if a single agent (called a leader)leaves its value unchanged,all others will asymptotically agree with the leader according to the consensus protocol and an alignment is reached.However,if there are multiple leaders where two of whom are in disagreement,then no consensus can be asymptot-ically reached.Therefore,alignment is in general a coop-erative task as well.Formal analysis of the behavior of systems that involve more than one type of agent is more complicated,partic-ularly,in presence of adversarial agents in noncooperative games [79],[80].The focus of this paper is on cooperative multi-agent systems.C.Iterative Consensus and Markov ChainsIn Section II,we show how an iterative consensus algorithm that corresponds to the discrete-time version of system (1)is a Markov chainðk þ1Þ¼ ðk ÞP(4)Olfati-Saber et al.:Consensus and Cooperation in Networked Multi-Agent SystemsVol.95,No.1,January 2007|Proceedings of the IEEE217with P ¼I À L and a small 90.Here,the i th element of the row vector ðk Þdenotes the probability of being in state i at iteration k .It turns out that for any arbitrary graph G with Laplacian L and a sufficiently small ,the matrix P satisfies the property Pj p ij ¼1with p ij !0;8i ;j .Hence,P is a valid transition probability matrix for the Markov chain in (4).The reason matrix theory [81]is so widely used in analysis of consensus algorithms [10],[12]–[15],[64]is primarily due to the structure of P in (4)and its connection to graphs.4There are interesting connections between this Markov chain and the speed of information diffusion in gossip-based averaging algorithms [77],[78].One of the early applications of consensus problems was dynamic load balancing [82]for parallel processors with the same structure as system (4).To this date,load balancing in networks proves to be an active area of research in computer science.D.ApplicationsMany seemingly different problems that involve inter-connection of dynamic systems in various areas of science and engineering happen to be closely related to consensus problems for multi-agent systems.In this section,we pro-vide an account of the existing connections.1)Synchronization of Coupled Oscillators:The problem of synchronization of coupled oscillators has attracted numer-ous scientists from diverse fields including physics,biology,neuroscience,and mathematics [83]–[86].This is partly due to the emergence of synchronous oscillations in coupled neural oscillators.Let us consider the generalized Kuramoto model of coupled oscillators on a graph with dynamics_i ¼ Xj 2N isin ð j À i Þþ!i (5)where i and !i are the phase and frequency of the i thoscillator.This model is the natural nonlinear extension of the consensus algorithm in (1)and its linearization around the aligned state 1¼...¼ n is identical to system (2)plus a nonzero input bias b i ¼ð!i À"!Þ= with "!¼1=n P i !i after a change of variables x i ¼ð i À"!t Þ= .In [43],Sepulchre et al.show that if is sufficiently large,then for a network with all-to-all links,synchroni-zation to the aligned state is globally achieved for all ini-tial states.Recently,synchronization of networked oscillators under variable time-delays was studied in [45].We believe that the use of convergence analysis methods that utilize the spectral properties of graph Laplacians willshed light on performance and convergence analysis of self-synchrony in oscillator networks [42].2)Flocking Theory:Flocks of mobile agents equipped with sensing and communication devices can serve as mobile sensor networks for massive distributed sensing in an environment [87].A theoretical framework for design and analysis of flocking algorithms for mobile agents with obstacle-avoidance capabilities is developed by Olfati-Saber [19].The role of consensus algorithms in particle-based flocking is for an agent to achieve velocity matching with respect to its neighbors.In [19],it is demonstrated that flocks are networks of dynamic systems with a dynamic topology.This topology is a proximity graph that depends on the state of all agents and is determined locally for each agent,i.e.,the topology of flocks is a state-dependent graph.The notion of state-dependent graphs was introduced by Mesbahi [64]in a context that is independent of flocking.3)Fast Consensus in Small-Worlds:In recent years,network design problems for achieving faster consensus algorithms has attracted considerable attention from a number of researchers.In Xiao and Boyd [88],design of the weights of a network is considered and solved using semi-definite convex programming.This leads to a slight increase in algebraic connectivity of a network that is a measure of speed of convergence of consensus algorithms.An alternative approach is to keep the weights fixed and design the topology of the network to achieve a relatively high algebraic connectivity.A randomized algorithm for network design is proposed by Olfati-Saber [47]based on random rewiring idea of Watts and Strogatz [89]that led to creation of their celebrated small-world model .The random rewiring of existing links of a network gives rise to considerably faster consensus algorithms.This is due to multiple orders of magnitude increase in algebraic connectivity of the network in comparison to a lattice-type nearest-neighbort graph.4)Rendezvous in Space:Another common form of consensus problems is rendezvous in space [90],[91].This is equivalent to reaching a consensus in position by a num-ber of agents with an interaction topology that is position induced (i.e.,a proximity graph).We refer the reader to [92]and references therein for a detailed discussion.This type of rendezvous is an unconstrained consensus problem that becomes challenging under variations in the network topology.Flocking is somewhat more challenging than rendezvous in space because it requires both interagent and agent-to-obstacle collision avoidance.5)Distributed Sensor Fusion in Sensor Networks:The most recent application of consensus problems is distrib-uted sensor fusion in sensor networks.This is done by posing various distributed averaging problems require to4In honor of the pioneering contributions of Oscar Perron (1907)to the theory of nonnegative matrices,were refer to P as the Perron Matrix of graph G (See Section II-C for details).Olfati-Saber et al.:Consensus and Cooperation in Networked Multi-Agent Systems218Proceedings of the IEEE |Vol.95,No.1,January 2007implement a Kalman filter [38],[39],approximate Kalman filter [74],or linear least-squares estimator [75]as average-consensus problems .Novel low-pass and high-pass consensus filters are also developed that dynamically calculate the average of their inputs in sensor networks [39],[93].6)Distributed Formation Control:Multivehicle systems are an important category of networked systems due to their commercial and military applications.There are two broad approaches to distributed formation control:i)rep-resentation of formations as rigid structures [53],[94]and the use of gradient-based controls obtained from their structural potentials [52]and ii)representation of form-ations using the vectors of relative positions of neighboring vehicles and the use of consensus-based controllers with input bias.We discuss the later approach here.A theoretical framework for design and analysis of distributed controllers for multivehicle formations of type ii)was developed by Fax and Murray [12].Moving in formation is a cooperative task and requires consent and collaboration of every agent in the formation.In [12],graph Laplacians and matrix theory were extensively used which makes one wonder whether relative-position-based formation control is a consensus problem.The answer is yes.To see this,consider a network of self-interested agents whose individual desire is to minimize their local cost U i ðx Þ¼Pj 2N i k x j Àx i Àr ij k 2via a distributed algorithm (x i is the position of vehicle i with dynamics _x i ¼u i and r ij is a desired intervehicle relative-position vector).Instead,if the agents use gradient-descent algorithm on the collective cost P n i ¼1U i ðx Þusing the following protocol:_x i ¼Xj 2N iðx j Àx i Àr ij Þ¼Xj 2N iðx j Àx i Þþb i (6)with input bias b i ¼Pj 2N i r ji [see Fig.1(b)],the objective of every agent will be achieved.This is the same as the consensus algorithm in (1)up to the nonzero bias terms b i .This nonzero bias plays no role in stability analysis of sys-tem (6).Thus,distributed formation control for integrator agents is a consensus problem.The main contribution of the work by Fax and Murray is to extend this scenario to the case where all agents are multiinput multioutput linear systems _x i ¼Ax i þBu i .Stability analysis of relative-position-based formation control for multivehicle systems is extensively covered in Section IV.E.OutlineThe outline of the paper is as follows.Basic concepts and theoretical results in information consensus are presented in Section II.Convergence and performance analysis of consensus on networks with switching topology are given in Section III.A theoretical framework for cooperative control of formations of networked multi-vehicle systems is provided in Section IV.Some simulationresults related to consensus in complex networks including small-worlds are presented in Section V.Finally,some concluding remarks are stated in Section VI.RMATION CONSENSUSConsider a network of decision-making agents with dynamics _x i ¼u i interested in reaching a consensus via local communication with their neighbors on a graph G ¼ðV ;E Þ.By reaching a consensus,we mean asymptot-ically converging to a one-dimensional agreement space characterized by the following equation:x 1¼x 2¼...¼x n :This agreement space can be expressed as x ¼ 1where 1¼ð1;...;1ÞT and 2R is the collective decision of the group of agents.Let A ¼½a ij be the adjacency matrix of graph G .The set of neighbors of a agent i is N i and defined byN i ¼f j 2V :a ij ¼0g ;V ¼f 1;...;n g :Agent i communicates with agent j if j is a neighbor of i (or a ij ¼0).The set of all nodes and their neighbors defines the edge set of the graph as E ¼fði ;j Þ2V ÂV :a ij ¼0g .A dynamic graph G ðt Þ¼ðV ;E ðt ÞÞis a graph in which the set of edges E ðt Þand the adjacency matrix A ðt Þare time-varying.Clearly,the set of neighbors N i ðt Þof every agent in a dynamic graph is a time-varying set as well.Dynamic graphs are useful for describing the network topology of mobile sensor networks and flocks [19].It is shown in [10]that the linear system_x i ðt Þ¼Xj 2N ia ij x j ðt ÞÀx i ðt ÞÀÁ(7)is a distributed consensus algorithm ,i.e.,guarantees con-vergence to a collective decision via local interagent interactions.Assuming that the graph is undirected (a ij ¼a ji for all i ;j ),it follows that the sum of the state of all nodes is an invariant quantity,or P i _xi ¼0.In particular,applying this condition twice at times t ¼0and t ¼1gives the following result¼1n Xix i ð0Þ:In other words,if a consensus is asymptotically reached,then necessarily the collective decision is equal to theOlfati-Saber et al.:Consensus and Cooperation in Networked Multi-Agent SystemsVol.95,No.1,January 2007|Proceedings of the IEEE219average of the initial state of all nodes.A consensus algo-rithm with this specific invariance property is called an average-consensus algorithm [9]and has broad applications in distributed computing on networks (e.g.,sensor fusion in sensor networks).The dynamics of system (7)can be expressed in a compact form as_x ¼ÀLx(8)where L is known as the graph Laplacian of G .The graph Laplacian is defined asL ¼D ÀA(9)where D ¼diag ðd 1;...;d n Þis the degree matrix of G with elements d i ¼Pj ¼i a ij and zero off-diagonal elements.By definition,L has a right eigenvector of 1associated with the zero eigenvalue 5because of the identity L 1¼0.For the case of undirected graphs,graph Laplacian satisfies the following sum-of-squares (SOS)property:x T Lx ¼12Xði ;j Þ2Ea ij ðx j Àx i Þ2:(10)By defining a quadratic disagreement function as’ðx Þ¼12x T Lx(11)it becomes apparent that algorithm (7)is the same as_x ¼Àr ’ðx Þor the gradient-descent algorithm.This algorithm globallyasymptotically converges to the agreement space provided that two conditions hold:1)L is a positive semidefinite matrix;2)the only equilibrium of (7)is 1for some .Both of these conditions hold for a connected graph and follow from the SOS property of graph Laplacian in (10).Therefore,an average-consensus is asymptotically reached for all initial states.This fact is summarized in the following lemma.Lemma 1:Let G be a connected undirected graph.Then,the algorithm in (7)asymptotically solves an average-consensus problem for all initial states.A.Algebraic Connectivity and Spectral Propertiesof GraphsSpectral properties of Laplacian matrix are instrumen-tal in analysis of convergence of the class of linear consensus algorithms in (7).According to Gershgorin theorem [81],all eigenvalues of L in the complex plane are located in a closed disk centered at Áþ0j with a radius of Á¼max i d i ,i.e.,the maximum degree of a graph.For undirected graphs,L is a symmetric matrix with real eigenvalues and,therefore,the set of eigenvalues of L can be ordered sequentially in an ascending order as0¼ 1 2 ÁÁÁ n 2Á:(12)The zero eigenvalue is known as the trivial eigenvalue of L .For a connected graph G , 290(i.e.,the zero eigenvalue is isolated).The second smallest eigenvalue of Laplacian 2is called algebraic connectivity of a graph [20].Algebraic connectivity of the network topology is a measure of performance/speed of consensus algorithms [10].Example 1:Fig.2shows two examples of networks of integrator agents with different topologies.Both graphs are undirected and have 0–1weights.Every node of the graph in Fig.2(a)is connected to its 4nearest neighbors on a ring.The other graph is a proximity graph of points that are distributed uniformly at random in a square.Every node is connected to all of its spatial neighbors within a closed ball of radius r 90.Here are the important degree information and Laplacian eigenvalues of these graphsa Þ 1¼0; 2¼0:48; n ¼6:24;Á¼4b Þ 1¼0; 2¼0:25; n ¼9:37;Á¼8:(13)In both cases, i G 2Áfor all i .B.Convergence Analysis for Directed Networks The convergence analysis of the consensus algorithm in (7)is equivalent to proving that the agreement space characterized by x ¼ 1; 2R is an asymptotically stable equilibrium of system (7).The stability properties of system (7)is completely determined by the location of the Laplacian eigenvalues of the network.The eigenvalues of the adjacency matrix are irrelevant to the stability analysis of system (7),unless the network is k -regular (all of its nodes have the same degree k ).The following lemma combines a well-known rank property of graph Laplacians with Gershgorin theorem to provide spectral characterization of Laplacian of a fixed directed network G .Before stating the lemma,we need to define the notion of strong connectivity of graphs.A graph5These properties were discussed earlier in the introduction for graphs with 0–1weights.Olfati-Saber et al.:Consensus and Cooperation in Networked Multi-Agent Systems220Proceedings of the IEEE |Vol.95,No.1,January 2007。

鸢尾花数据集为例实现贝叶斯算法和感知器算法

鸢尾花数据集为例实现贝叶斯算法和感知器算法贝叶斯算法：贝叶斯算法是一种基于概率的分类算法。

它假设样本特征之间相互独立，并根据贝叶斯定理计算后验概率来进行分类。

在鸢尾花数据集上实现贝叶斯算法的步骤如下：1. 导入数据集：从鸢尾花数据集中获取特征和标签。

2. 数据预处理：对特征进行标准化或归一化处理，以便特征具有相似的尺度。

3. 拆分数据集：将数据集划分为训练集和测试集，用于模型的训练和评估。

4. 计算先验概率：根据训练集中的标签计算每个类别的先验概率。

5. 计算类条件概率：对于每个特征和每个类别，计算其类条件概率。

6. 预测分类：使用贝叶斯公式，结合先验概率和类条件概率，计算后验概率，选择具有最高后验概率的类别作为预测结果。

7. 评估模型：使用测试集评估模型的性能，可以计算准确度或其他评价指标。

感知器算法：感知器算法是一种简单的分类算法，可以用于二分类问题。

该算法通过迭代更新模型的权重和偏置，使模型能够学习将数据分为不同类别。

在鸢尾花数据集上实现感知器算法的步骤如下：1. 导入数据集：从鸢尾花数据集中获取特征和标签。

2. 数据预处理：对特征进行标准化或归一化处理，以便特征具有相似的尺度。

3. 初始化权重和偏置：随机初始化模型的权重和偏置。

4. 迭代更新模型：对于每个样本，计算模型的预测值，与真实标签进行比较，根据预测的正确与否，更新权重和偏置。

5. 重复步骤4，直到达到预先定义的停止条件（例如达到最大迭代次数或预测准确率达到一定阈值）。

6. 评估模型：使用测试集评估模型的性能，可以计算准确度或其他评价指标。

需要注意的是，在实际应用中，贝叶斯算法和感知器算法可能需要进行一些改进和优化，以提高分类的准确性和性能。

以上只是一种简单的实现示例。

生物信息学实验指导实验二Ensemble使用word精品

实验二Ensemble 使用1.1在Ensemble 页面All genomes 的下拉菜单中选择 human ,查看这个物种的具体信息，人的染色体和基因数量如图所示，基因数量主要看 Alternative sequenee的图示。

gen etie variation 有 Short Variants ( 329,179,721)和 Structural varia nts (5,955,877)。

Coding genes20.338 (incl &G2 n&adthrough) Non coding goriM22 521Small non coding genes 5,363Lona nori coding genes 14.720 (ind 23S reacJthrouph) Misc non coding genes 2.222P&eudogenes 14.638 (incl 6 readthrough;Gene transcripts200,310Gene counts (Alternative sequence)Short Variants Structural variants1.2在Ensemble 首页进行 human for MAPK4 搜索，在结果页面追加 Restrict category to 为gene,筛选到117条序列，打开登录号为 ENSG00000141639的目标序歹U ，查看 Gene-based displays1.2.1这个基因有6个可变剪接，他们之间序列长度不同，其中 4个可以编码蛋白，所编码蛋白的氨基酸数量也不同。

1.2.2 在 Comparative Genomics 项 Genomic alignments 中，选择 multiple ，然后选择 27 种 am ni ota vertebrates Pecans 行比对，在 con figure this page 中勾选 ShowCoding ^enes NlCn coding geri 亡吝Small non coding genes Long non coding gwn 四 Misc nan coding g^nes2,750 (inc I 37 neadth rough) 1.288242877 :incl 33 rsidthrough1591600Gen&can gene predictions 60.781329,V9 721 5.955.877Gene counts (Primary assembly}conservation regions 在Alignments (text)部分，可以看到蓝色高亮显示的保守区域了。

ensemble分类

ensemble分类Ensemble分类是一种常见的机器学习技术，它结合了多个分类算法来提高分类准确性。

该技术在近年来的机器学习领域取得了很大的进展，成为许多机器学习应用中的首选技术之一。

下面将分步骤阐述ensemble分类的基本原理和应用方法。

1. 基本原理Ensemble分类是将多个分类算法组合起来，形成一个更为强大的分类器。

这里的“强大”是指它能够更准确地识别和分类样本。

当多个算法组合起来进行分类时，每个算法都会投票决定最终分类结果。

假设有10个算法组成了一个ensemble分类器，其中有6个算法将样本分类为A，3个算法将样本分类为B，1个算法将样本分类为C，那么ensemble分类器将最终将该样本分类为A。

2. 应用方法在使用ensemble分类器时，有许多方法可以用来创建它们。

以下是一些基本的方法：- Bagging（Bootstrap Aggregation）：这种方法是从训练数据中随机采样一些数据形成一个新的训练集。

重复这个过程多次，每次采样的训练集都是从原始数据集中有放回地抽样得到的。

然后训练多个分类器，最后投票决定分类结果。

- Boosting：这种方法是通过构建一系列简单的分类器，将它们组合起来来形成一个强大的分类器。

Boosting算法中的每个分类器都会尝试修改训练数据，使其更难分类，显然这会在每个新的分类器中产生更多有用的信息。

- Stacking：这种方法是使用一个类型的分类器来组合其他不同类型或相同类型的分类器。

集成的分类器使用其他训练好的分类器的输出作为输入，而没有像在Bagging和Boosting中那样处理输入数据。

3. 优点和局限性Ensemble分类器的主要优点是它能够更准确地分类数据，并且克服了单个分类器在某些情况下的不足。

另外，由于ensemble分类器是由多个分类器组成的，因此它们对于训练中的噪声和错误更具有鲁棒性。

然而，ensemble分类器的局限性在于它们可能需要更长的训练时间来训练多个分类器。

大模型推理的ensemble 方案

大模型推理的ensemble 方案
大模型推理的ensemble方案是一种集成学习的方法，其基本思想是将多个模型的预测结果进行组合，以提高整体的预测精度和稳定性。

以下是几种常见的ensemble方案：
1. Bagging：Bagging采用自助采样法从数据集中有放回地随机抽取样本，并训练多个基模型，然后对这些基模型进行加权平均或投票。

Bagging可
以降低模型的方差并提高模型的泛化能力。

2. Boosting：Boosting是一种迭代算法，它通过将多个弱学习器组合成一个强学习器来提高预测精度。

在每一步迭代中，Boosting算法会关注那些
在前面步骤中容易出错的样本，并让弱学习器集中精力学习这些样本。

常见的Boosting算法有AdaBoost、Gradient Boost等。

3. Stacking：Stacking是一种分层集成方法，它通过将多个基模型组合成
一个元模型来提高预测精度。

在训练元模型时，我们使用基模型的预测结果作为新的输入特征，并训练一个新的基模型来预测最终的输出。

Stacking
可以进一步降低模型的方差并提高泛化能力。

4. Blending：Blending是一种将多个模型的结果进行线性组合的方法，以提高预测精度和稳定性。

在Blending中，我们使用不同的模型或不同的特
征子集来预测同一个样本，并将这些预测结果进行加权平均或投票。

Blending的优点是简单易实现，并且可以充分利用各种模型的优势。

以上是几种常见的ensemble方案，它们都可以用于提高大模型的推理性能。

具体选择哪种方案取决于数据集、任务和模型的特点。

基于内容的细菌病毒检索系统设计

第43卷第3期2021年3月宜春学院学报Journal of Yichun UniversityVol.43(No.3Mar.2021基于内容的细菌病毒检索系统设计陈军（宜春学院数学与计算机科学学院，江西宜春336000）摘要：目前人类对于致病细菌、病毒的认识还停留在语义描述阶段，比较缺乏形象的、可视化的描述方式。

因此也很难对病毒、细菌形成较深刻的印象。

而且对于现有的致病性细菌、病毒也缺乏系统的可视化存储，这非常不利于人类对于致病性细菌、病毒的了解。

在此需求下开发一个可视化基于内容的病毒检测系统可以很好解决上述问题。

系统建设主要包括病毒图片获取、病毒图片特征提取、病毒库索引生成、病毒图片相似性分析对比、检索反馈及结果输出等功能模块。

该系统可以通过爬虫等技术手段采集病毒图片，生成病毒库，从而对新型病毒或细菌进行基于内容的检索，从而有利于对病毒或细菌的种族、类型等进行研究和分析。

关键词：基于内容；检索；病毒；系统；爬虫中图分类号：TP391文献标识码：A文章编号：1671-380X（2021）03-0050-05Design of Content-based Bacterial and Virus Retrieval SystemCHEN Jun(College of Mathematics and Computer Science,Yichun University,Yichun336000,China) Abstracr:At present,thr understanding of pdthogenic bacteric and virusrs I still at thr stdgr of semantic drscription,lacking of visual description.Therefore,it is difficult ta br impressed by viruses and bactriv.And for the existing pataogenic bacterio and virusrc,there it alse a lack of systematic visual storagr,which it not conducive to human understanding of pathogenic bacteao and vinsee.In ordee to solvv these probleme,we need to de-vlop a visual content一based virne detection s ystem. System construction mainly includee virue image acquisition, v ceu ecmage oeatu ee e it ea tt con,v ceu edataba ee cnde i gene eat con,vceuecmageecmcaaectyanaayeceand tompaeceon, 0106X41feedback and result output and other functional modulee.The system con collect virne picturee and弐的巩-ate vine VPaa by reptile ichnology,se ae te seerch new vine or bacteav based on content,which ie conducive te tee reseerch and analysis of tee neo and type of vine or bacteav.Key worUt:content based;比!^'^;virue;system；reptile随着互联网的兴起，尤其是手机等移动通讯设备的普及，以图片、声音、视频等形式为主体的多媒体技术［1］越来越多的应用于各行各业。

stable diffusion 微调生成训练集

稳定扩散（Stable Diffusion）在机器学习中扮演着至关重要的角色。

它指的是通过微调（Fine-tuning）生成一个高质量的训练集。

在本文中，我们将深入探讨稳定扩散的概念，以及微调生成训练集的重要性和方法。

让我们从稳定扩散的概念说起。

稳定扩散是指在机器学习模型中，通过微调对数据进行扩散和洗牌，使得模型能够更好地适应不同的数据分布。

这对于提高模型的鲁棒性和泛化能力至关重要。

稳定扩散能够减少模型对特定训练集的过度依赖，从而提高模型在实际应用中的性能。

接下来，让我们详细讨论微调生成训练集的过程。

微调是指使用已经训练好的模型，在特定任务上进行进一步的训练，以提高模型在该任务上的表现。

在微调过程中，我们通常会选择一个具有大量数据和高质量标签的数据集作为训练集。

然而，在实际应用中，我们往往会面临数据分布不均匀、标签质量参差不齐等问题，这就需要我们通过稳定扩散的方式来生成一个更加稳定和高质量的训练集。

为了实现稳定扩散和微调生成训练集，我们需要考虑以下几点。

我们需要对原始数据进行深入的分析，了解数据的分布情况、数据的质量以及标签的准确性。

我们需要选择合适的数据扩增方法，以增加数据的多样性和数量，从而提高模型的泛化能力。

我们还需要设计合适的数据筛选和清洗方法，以去除噪声和异常样本，使得训练集更加稳定和高质量。

在实际操作中，稳定扩散和微调生成训练集需要有一定的经验和技巧。

在这个过程中，我们需要不断地调整参数、尝试不同的方法，并通过实践积累经验。

我们也需要关注模型在不同训练集上的表现，及时调整并提升训练集的质量。

总结来说，稳定扩散和微调生成训练集在机器学习中扮演着至关重要的角色。

通过稳定扩散，我们可以生成一个更加稳定和高质量的训练集，从而提高模型在实际应用中的性能。

然而，在实际操作中，我们需要有一定的经验和技巧，并通过不断的实践来提升自己的能力。

希望通过本文的探讨，读者能对稳定扩散和微调生成训练集有更加深入和灵活的理解。

大学管理时间英语作文

Effective time management is a crucial skill for university students.It not only helps in maintaining a balance between academic and personal life but also enhances productivity and reduces stress.Here are some strategies that can be employed to manage time efficiently during university years:1.Set Clear Goals:Start by setting both longterm and shortterm goals.This will give youa clear direction and help you prioritize tasks.2.Create a Schedule:Use a planner or digital calendar to map out your daily,weekly,and monthly schedules.Allocate specific time slots for classes,study sessions,and leisure activities.3.Prioritize Tasks:Not all tasks are equally e the Eisenhower Matrix to categorize tasks into four quadrants based on their urgency and importance.4.Break Down Large Tasks:Divide large assignments or projects into smaller, manageable tasks.This makes them less daunting and easier to tackle.5.Avoid Procrastination:Procrastination can lead to lastminute stress and poor performance.Stay focused on your tasks and avoid distractions.e Time Management Tools:Utilize apps and tools designed for time management, such as timers,todo lists,and project management software.7.Stay Organized:Keep your study space and materials organized.This reduces the time spent searching for notes or textbooks.8.Practice SelfDiscipline:Discipline is key to sticking to your schedule and avoiding the temptation to procrastinate.9.Learn to Say No:Its important to recognize your limits and not overcommit to activities that could interfere with your study time.10.Take Regular Breaks:Short breaks can improve focus and e techniques like the Pomodoro Technique,which involves working for25minutes followed by a5minute break.11.Reflect on Your Progress:Regularly review your schedule and time management strategies to identify what works and what doesnt,and make necessary adjustments.12.Seek Support:If youre struggling with time management,dont hesitate to seek help from academic advisors,tutors,or peers.13.Maintain a Healthy Lifestyle:Proper nutrition,exercise,and sleep are essential for maintaining the energy and focus needed for effective time management.14.Be Flexible:Understand that plans may change and be prepared to adapt your schedule accordingly.15.Reward Yourself:Set up a reward system for completing tasks or achieving goals. This can motivate you to stay on track.By implementing these strategies,university students can better manage their time, leading to improved academic performance and a more balanced life.Remember, effective time management is a skill that can be developed and refined over time with practice and selfawareness.。

Ensembl数据库简介

Ensembl数据库说明1、简介Ensembl是一个由英国Sanger研究所Wellcome基金会（WTSI）和欧洲分子生物学实验室所属分部欧洲生物信息学研究所（EMBI-EBI）共同协作运营的一个项目。

这些机构均位于英国剑桥市南部辛克斯顿的威康信托基因组校园（Wellcome Trust Genome Campus）内。

Ensembl计划开始于1999年，人类基因组草图计划完成前的几年。

即使在早期阶段，也可明显看出，三十亿个碱基对的人工注释是不能够为科研人员提供实时最新数据的获取的。

因此Ensembl的目标是自动的基因组注释，并把这些注释与其他有用的生物数据整合起来，通过网络公开给所有人使用。

Ensembl数据库网站开始于July 2000，是一个真核生物基因组注释项目，其侧重于脊椎动物的基因组数据，但也包含了其他生物，如线虫，酵母，拟南芥和水稻等[4]。

近年来，随着时间推移，越来越多的基因组数据已经被添加到了Ensembl，同时Ensembl可用数据的范围也扩展到了比较基因组学、变异，以及调控数据。

参与Ensembl计划的人也在稳步增加，目前Ensembl的组员有40到50个人，分成几个小组。

Genebuild小组负责不同物种的gene sets创建。

他们的结果被保存在核心数据库中，该数据库由Software小组进行运维。

Software小组还负责BioMart数据挖掘工具的开发和维护。

Compara、Variation以及Regulation小组分别负责比较组学、突变以及调控的数据相关工作。

Web小组的工作是确保所有的数据能够在网站页面上，通过清晰和友好用户界面呈现出来。

最后，Outreach小组负责用户的答疑，以及提供全球范围内使用Ensembl 的研讨会议或知识培训。

[1]2、Ensembl数据08 Dec 2015，Ensembl发发布了最新的Ensembl 83版本数据。

①ftp:///pub/release-83/variation/vcf/homo_sapiens/②192.168.174.69:/Share/database/pub//pub/release-83/variation/vcf/homo_sapiens（izbox，全部数据约5.2G）3、突变信息（Ensembl Variation）The Ensembl Variation database stores areas of the genome that differ between individual genomes ("variants") and, where available, associated disease and phenotype information。

Ensemble Postprocessing数据集说明书

Package‘ensemblepp’October13,2022Title Ensemble Postprocessing Data SetsVersion1.0-0Date2019-05-03Author Jakob Messner[aut,cre]Maintainer Jakob Messner<************************>Depends R(>=2.10.0)Suggests ensembleBMA,crch,gamlss,ensembleMOS,SpecsVeriﬁcation,scoringRules,glmx,ordinal,pROC,mvtnormDescription Data sets for the chapter``Ensemble Postprocessing with R''of the book Stephane Van-nitsem,Daniel S.Wilks,and Jakob W.Messner(2018)``Statistical Postprocessing of Ensem-ble Forecasts'',Elsevier,362pp.These data sets contain temperature and precipitation ensem-ble weather forecasts and corresponding observations at Innsbruck/Austria.Addition-ally,a demo with the full code of the book chapter is provided.License GPL-2|GPL-3NeedsCompilation noRepository CRANDate/Publication2019-05-0807:50:10UTCR topics documented:rain (2)temp (3)Index512rain rain Precipitation Observations and Forecasts for InnsbruckDescriptionAccumulated18-30hour precipitation ensemble forecasts and corresponding observations at Inns-bruck.The dataset includes GEFS reforecasts(Hamill et al.2013)and observations from SYNOP station Innsbruck Airport(11120)from2000-01-02to2016-01-01.Usagedata("temp")FormatA data frame with2749rows.Theﬁrst column(rain)are12-hour accumulated precipitation ob-servations.Columns2-12(rainfc)are18-30hour accumulated precipitation forecasts from the individual ensemble members.SourceObservations:/synops.phtml.enReforecasts:/psd/forecasts/reforecast2/ReferencesHamill TM,Bates GT,Whitaker JS,Murray DR,Fiorino M,Galarneau Jr TJ,Zhu Y,Lapenta W(2013).NOAA’s Second-Generation Global Medium-Range Ensemble Reforecast Data Set.Bulletin of the American Meteorological Society,94(10),1553-1565.Vannitsem S,Wilks DS,Messner JW(2017).Statistical Postprocessing of Ensemble Forecasts, Elsevier,to appear.Examples##Diagnostic plots similar to Figure8in Vannitsem et al.####load and prepare datadata("rain")rain<-sqrt(rain)rain$ensmean<-apply(rain[,2:12],1,mean)rain$enssd<-apply(rain[,2:12],1,sd)##Scatterplot of precipitation by ensemble meanplot(rain~ensmean,rain,col=gray(0.2,alpha=0.4),main="Scatterplot")abline(0,1,lty=2)temp3 ##Verification rank histogramrank<-apply(rain[,1:12],1,rank)[1,]hist(rank,breaks=0:12+0.5,main="Verification Rank Histogram")##Spread skill relationshipsdcat<-cut(rain$enssd,quantile(rain$enssd,seq(0,1,0.2)))boxplot(abs(rain-ensmean)~sdcat,rain,ylab="absolute error",xlab="ensemble standard deviation",main="Spread-Skill")##Histogramhist(rain$rain,xlab="square root of precipitation",main="Histogram")temp Minimum Temperature Observations and Forecasts for InnsbruckDescription18-30hour minimum temperature ensemble forecasts and corresponding observations at Innsbruck.The dataset includes GEFS reforecasts(Hamill et al.2013)and observations from the SYNOP station Innsbruck Airport(11120)from2000-01-02to2016-01-01.Usagedata("temp")FormatA data frame with2749rows.Theﬁrst column(temp)are12-hour minimum temperature observa-tions.Columns2-12(tempfc)are18-30hour minimum temperature forecasts from the individual ensemble members.SourceObservations:/synops.phtml.enReforecasts:/psd/forecasts/reforecast2/ReferencesHamill TM,Bates GT,Whitaker JS,Murray DR,Fiorino M,Galarneau Jr TJ,Zhu Y,Lapenta W(2013).NOAA’s Second-Generation Global Medium-Range Ensemble Reforecast Data Set.Bulletin of the American Meteorological Society,94(10),1553-1565.Vannitsem S,Wilks DS,Messner JW(2017).Statistical Postprocessing of Ensemble Forecasts, Elsevier,to appear.4tempExamples##Diagnostic plots similar to Figure1and3in Vannitsem et al.####load and prepare datadata("temp")temp$ensmean<-apply(temp[,2:12],1,mean)temp$enssd<-apply(temp[,2:12],1,sd)##Scatterplot of minimum temperature observation by ensemble meanplot(temp~ensmean,temp,main="Scatterplot")abline(0,1,lty=2)##Verification rank histogramrank<-apply(temp[,1:12],1,rank)[1,]hist(rank,breaks=0:12+0.5,main="Verification Rank Histogram")##Spread skill relationshipsdcat<-cut(temp$enssd,breaks=quantile(temp$enssd,seq(0,1,0.2)))boxplot(abs(temp-ensmean)~sdcat,temp,ylab="absolute error",xlab="ensemble standard deviation",main="Spread-Skill")##Histogramhist(temp$temp,xlab="minimum temperature",main="Histogram")Index∗datasetsrain,2temp,3rain,2temp,35。

Biopython中Entrez模块--从pubmed中查找相关文献，所有返回的结果用En。。。

Biopython中Entrez模块--从pubmed中查找相关⽂献，所有返回的结果⽤En。

Entrez是⼀个搜索引擎，国家⽣物技术信息中⼼（NCBI）⽹站集成了⼏个健康科学的数据库，如：如“科学⽂献，DNA和蛋⽩质序列数据库，蛋⽩质三维结构，蛋⽩质结构域的数据，表达数据，基因组完整拼接本等。

Entrez的编程⼯具”（eUtils）：通过它把搜索的结果返回到⾃⼰编写的程序⾥⾯，需要提供URL，并且⾃⼰解析XML⽂件。

Entrez模块，利⽤该模块可以省去提供URL和解析XML的步骤。

Entrez模块中的函数，同时也是eUtils中具有的⼀些函数：从pubmed中查找相关⽂献，所有返回的结果⽤Entrez.read()解析from Bio import Entrezmy_em = 'user@' db = "pubmed"# Search Entrez website using esearch from eUtils# esearch returns a handle (called h_search) 主要⽤来返回id，h_search = Entrez.esearch(db=db, email=my_em, term="python and bioinformatics")record = Entrez.read(h_search) # Parse the result with Entrez.read(),record是字典res_ids = record[“IdList”] # Get the list of Ids returned by previous search. 该键的值是列表# For each id in the listfor r_id in res_ids:# Get summary information for each id h_summ = Entrez.esummary(db=db, id=r_id, email=my_em) # Parse the result with Entrez.read() summ = Entrez.read(h_summ) #返回⼀个列表，第⼀个元素是字典，不同的数据库返回的数据的结构不⼀样 print(summ[0]['Title']) print(summ[0]['DOI']) print('==============================================')结果：do_x3dna: A tool to analyze structural fluctuations of dsDNA or dsRNA from molecular dynamics simulations.10.1093/bioinformatics/btv190==============================================RiboTools: A Galaxy toolbox for qualitative ribosome profiling analysis.10.1093/bioinformatics/btv174==============================================Identification of cell types from single-cell transcriptomes using a novel clustering method. 10.1093/bioinformatics/btv088 ==============================================Efficient visualization of high-throughput targeted proteomics experiments: TAPIR.10.1093/bioinformatics/btv152。