Learning Traversability Models for Autonomous Mobile Vehicles, Autonomous Robots (submitted
个性化教学认知风格模型与测量
５１
探
索
与
实
践
多项研究得出十分相近的结果 ! Ｚｙｗ ｎｏ， ２００３" # 表 ４． １ 给 出 了 对 工 程 专 业 学 生 进 行 Ｆｅｌｄｅｒ－ Ｓｏｌｏｍａｎ 的学习风格量表测试结果的分布 $
值 #而活跃型和序列型则稍低 $ 见图 ４． １ 所示 $
表 !"# 工程专业学生 $%&’%()*+&+,-. 的学习 风格量表测试结果分布 !三 " 修订后的 !"#$"% 模型的实验 由于国内的学生文化背景 % 教育环境和教学方 法的差异 #在使用 Ｆｅｌｄｅｒ－ Ｓｏｌｏｍａｎ 的学习风格量表 测试时 # 首先对量表进行了一定的修订 # 以适合国内 学生情况 $ 在修订过程中主要考虑两个因素 & ! %淡化文化背景的差异 $ 在内容取材上 # 摆脱西 方测验的影响 # 从中国民族文化中寻找素材 # 这样才 能使科学测验与被试之间的距离缩短 # 更适合中国 的被试 #易于被理解和接受 $ " % 减少语言理解对问题回答的影响 $ 在试题表 述方式上 # 按照汉语的语言习惯 # 准确传达测试要素 的内涵 # 题目一目了然 # 选项简洁明了 # 使被试不因 为理解题目而增加额外的思考负荷 $
方式之间的关系 ， 以及学生在信息加工过程中表现 出来的认知差异 ( & +认知风格和学习材料表征方式之间的关系 实验结果表明 ： 言语型的学习者在文本形式的 信息表征中存在学习优势 ， 而表象型的学习者在图 像形式的信息表征中存在学习优势 ( 另一项实验结 果也表明 ： 言语型学生对语义性材料的回忆成绩比 表象型学生好 ， 而表象型的学习者对于形象性材料 的回忆成绩比言语型的好 ( 这就证明： 学生的认知风 格和学习材料 的 表 征 方 式 之 间 存 在 着 显 著 的 相 关 ， 即学生的认知风格与学习材料的表征方式匹配与 否 ， 将直接影响学习结果 ( 或者说 ， 不同认知风格的 学生对学习材料的表征方式有一定的适应性 ( 因此， 在教学过程中 ， 教师要注意提供多种表征方式的学 习 材 料 ， 如 文 本 $ 图 像 或 文 本* ** 图 像 等 表 征 方 式 ， 以提高教学效果 ( !$学生在信息加工过程中表现出来的认知差异 个体在信息加工时也存在个体差异 ( Ｒｉｄｉｎｇ 等 人 １９９３ 年对 ７７ 个 １１ 岁的学生进行一项研究 ， 要求 他们对一段短文加以回忆 ( 结果发现： 就整体型学生 而言 ， 当散文题目放在正文之前时 ， 其回忆成绩较之 将散文题目放在正文之后更好 ； 而双极型 （ 介于整体 型和分析型之间） 和分析型学生对题目的位置并不敏 感 ( 还有一项心理实验也得出了类似的结论 ( 拉埃丁 等人抽取了 ２００ 个 １０!１５ 岁的学生进行一项研究 ， 要求男女学生分别在两种情况下回忆两篇同样内容 的短文 ， 一种情况是短文未经任何处理 ； 另一种情况 是已将短文细分为三段 $而且每段都有次标题 ( 结果 发现 ： 后一种情况大大促 进 回 忆 成 绩 的 提 高 ， 而 且 ， 提高幅度受到认知风格和性别的影响 ( 以上实验都 证明 ： 个体在信息加工存在不同的倾向 ， 而且个体加 工方式和不同 的 学 习 材 料 之 间 存 在 一 定 的 相 关 性 ( 这就提醒教师在教学中要考虑到学生的认知风格差 异 ， 并针对不同的特点采取不同的教学策略 ( , 二 #’()#(* 模型的教学实验 Ｍｉｃｈ ｉｇａｎ 大 学 的 Ｍｏｎｔｇｏｍｅｒｙ 比 较 了 Ｋｏｌｂ $ Ｍｙｅｒｓ－ Ｂｒｉｇｇｓ 和 Ｆｅｌｄｅｒ－ Ｓｏｌｏｍａｎ 的学习风格量表 , Ｍｏｎｔｇｏｍｅｒｙ， １９９５# ! 研究表明 Ｋｏｌｂ 的学习风格 量 表 含 有 太 多 的 术 语 致 使 回 答 起 来 很 困 难 !Ｍｙ－ ｅｒｓ－ Ｂｒｉｇｇｓ 学 习 风 格 量 表 主 要 着 重 个 性 的 测 试 ! 而 削弱了对学习风格的研究效果 !Ｆｅｌｄｅｒ－ Ｓｏｌｏｍａｎ 的 学习风格量表学生感到易回答 ( 该量表可操作性强 ! 学习者可以自我测定 ! 利用 Ｆｅｌｄｅｒ－ Ｓｏｌｏｍａｎ 的学习 风格量表检测不同学习风格的分布情况 ! 国际上有
ai模型训练相关英文术语解释
ai模型训练相关英文术语解释1. Artificial Intelligence (AI): The theory and development of computer systems that can perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and problem-solving.2. Model Training: The process of teaching an AI model to learn patterns and make predictions or decisions by providing it with a large amount of training data and adjusting the model's internal parameters or structure.3. Training Data: The data used to train an AI model. It typically consists of input data and corresponding target output data that is used to guide the learning process.4. Labeling: The process of annotating or categorizing data for training an AI model. Labels provide ground truth information about the data and help the model learn to recognize patterns and make accurate predictions.5. Supervised Learning: A type of machine learning where the AI model is trained using labeled examples, meaning there is a known correct answer provided for each input data point.6. Unsupervised Learning: A type of machine learning where theAI model is trained using unlabeled data. The model is expected to find patterns or structures in the data without any explicit guidance.7. Reinforcement Learning: A type of machine learning where an AI model learns to make decisions or take actions in anenvironment to maximize a reward signal. The model learns through trial and error, receiving feedback on the quality of its actions.8. Neural Network: A type of model architecture inspired by the human brain. It consists of interconnected nodes (neurons) organized in layers, with each neuron performing a simple computation. Neural networks are commonly used in deep learning.9. Deep Learning: A subfield of machine learning that focuses on artificial neural networks with multiple layers. Deep learning allows for the learning of hierarchical representations of data, enabling the model to process complex patterns and relationships.10. Loss Function: A function that measures the discrepancy between the predicted outputs of an AI model and the true target outputs. During training, the model aims to minimize this discrepancy by adjusting its internal parameters.11. Gradient Descent: An optimization algorithm used to minimize the loss function in training an AI model. It calculates the gradient of the loss function with respect to the model parameters and updates them in the direction of steepest descent.12. Overfitting: A phenomenon that occurs when an AI model performs well on the training data but poorly on new, unseen data. It happens when the model becomes too specialized in capturing the noise or specific patterns of the training data, resulting in poor generalization.13. Hyperparameters: Parameters that define the configuration of an AI model and affect its learning process, but are not directly learned from the training data. They include parameters such as learning rate, number of layers, and activation functions.14. Validation Set: A portion of the training data that is set aside and not used for training the model. It is used to evaluate the performance of the model during the training process and tune the hyperparameters.15. Test Set: A separate dataset used to evaluate the final performance of the trained AI model. It consists of data that the model has never seen before and is used to assess the model's ability to generalize to new, unseen data.。
learningrate参数 -回复
learningrate参数-回复什么是学习率（learning rate）参数？学习率是机器学习中的一个重要参数，用于控制模型的学习速度或者说梯度下降的步长。
它决定了模型在每次迭代中更新权重的幅度。
学习率的选择很关键，过小的学习率会导致模型收敛缓慢，而过大的学习率可能会导致模型无法达到最优解。
在机器学习算法中，我们通常会最小化一个损失函数，以找到最佳的模型参数。
梯度下降是一种常用的优化算法，它通过计算损失函数对参数的梯度来更新模型的参数。
学习率的大小决定了更新参数时的步伐大小。
如果学习率较大，模型参数会在每次迭代中做较大幅度的更新，但可能会错过最优解；如果学习率较小，模型参数的更新幅度较小，收敛速度会变慢。
如何选择合适的学习率参数？选择合适的学习率参数是一项重要的任务，因为它直接影响模型的性能和训练速度。
下面介绍几种常用的方法来选择合适的学习率参数。
1. 固定学习率：在训练过程中，将学习率设定为一个固定的常数。
这种方法简单直接，但可能需要大量的实验来确定最佳的学习率值。
2. 学习率衰减（learning rate decay）：在训练的过程中逐渐降低学习率。
一种常用的学习率衰减策略是每个epoch（所有训练样本都被遍历一次）将学习率减小一个固定的因子（如0.1），以逐渐降低学习率。
3. 学习率重启（learning rate restarts）：在训练过程中周期性地重置学习率。
例如，可以在每个epoch结束时重设学习率为一个较大的值，以帮助模型跳出局部最优解。
4. 学习率自适应（learning rate adaptive）：根据模型训练的进展动态调整学习率。
一种常见的自适应方法是AdaGrad，它根据参数的梯度历史信息自适应地调整学习率。
还有其他一些自适应算法，如Adam、RMSProp 等。
5. 学习率调度（learning rate scheduling）：根据训练的进展和性能动态地调整学习率。
半监督学习中的自训练方法详解(七)
在机器学习领域，半监督学习是一种重要的学习范式，它结合了监督学习和无监督学习的优点，能够在数据标记有限的情况下实现高效的模型训练。
在半监督学习中，自训练（self-training）方法是一种常见且有效的技术，它通过迭代式的自我训练来利用未标记数据提升模型性能。
本文将详细介绍半监督学习中的自训练方法，包括其基本原理、算法流程、应用场景以及优缺点。
自训练的基本原理是利用已训练好的模型对未标记数据进行预测，并将预测结果置信度较高的样本作为伪标记加入训练集，然后重新训练模型，不断迭代直至收敛。
在每一轮迭代中，模型都会逐渐提升预测性能，并且不断扩充训练数据，从而实现半监督学习的目标。
这种方法充分利用了未标记数据的信息，弥补了标记数据不足的问题，因此在实际应用中具有广泛的价值。
具体来说，自训练方法通常包括以下几个步骤。
首先，使用已标记的数据对模型进行初始化训练。
然后，利用训练好的模型对未标记数据进行预测，得到预测结果和置信度。
接着，根据置信度，将置信度较高的样本加入训练集，并标记为伪标记。
最后，使用扩充后的训练集重新训练模型，重复上述步骤，直到模型收敛或达到迭代次数。
自训练方法在实际应用中有着广泛的应用场景。
首先，对于一些领域数据标记困难或成本较高的任务，例如医疗影像分析、自然语言处理等，自训练方法可以通过利用大量的未标记数据来提升模型性能，从而降低标记成本。
其次，在数据分布漂移或标记不一致的情况下，自训练方法也能够通过迭代式的学习来适应新的数据分布和标记规律，保持模型的泛化能力。
此外，自训练方法还可以与其他半监督学习方法相结合，形成更加复杂和高效的模型训练流程。
然而，自训练方法也存在一些缺点和挑战。
首先，自训练方法对于初始标记数据的质量和数量非常敏感，如果初始标记数据质量较差或数量过少，可能会导致模型性能下降。
其次，自训练方法对于置信度的估计非常关键，如果置信度估计不准确，可能会导致伪标记的错误引入，进而影响模型性能。
reward model的结构
"Reward model"在机器学习和强化学习的背景下通常指的是一个用来评估特定行为或决策的好坏的模型。
在不同的环境和应用中，reward model的结构可能会有很大的不同。
以下是几种可能的结构：1. 表格型（Tabular）Reward Model：在这种结构中，对于每一个状态（或状态-动作对），模型都会有一个对应的reward值。
这种模型通常适用于状态空间和动作空间都相对较小的情况。
2. 函数逼近（Function Approximation）Reward Model：当状态或动作的空间很大或者是连续的时候，表格型表示就变得不切实际。
函数逼近方法，如线性回归、神经网络（例如深度Q网络，DQN），或高斯过程等被用来近似reward函数。
这允许模型根据输入的状态和/或动作预测reward。
3. 基于策略的（Policy-based）Reward Model：在某些情况下，reward模型可能与策略模型结合在一起，这样它们可以共同更新以提高性能。
这种类型的模型通常使用策略梯度方法或演员-评论家（Actor-Critic）方法。
4. 倒置强化学习（Inverse Reinforcement Learning，IRL）Reward Model：在IRL中，reward模型是通过观察一个专家或者优化的行为来推断的。
这种情况下，reward模型会尝试复现专家行为所隐含的reward结构。
5. 学习型Reward Model：有时候，reward模型是通过学习得到的，例如，它可能通过人类反馈（如偏好学习、评分系统）来调整和优化。
这种模型可能包含多种机器学习技术，包括监督学习、半监督学习或强化学习。
6. 基于模型的（Model-based）Reward Model：在基于模型的强化学习中，reward模型通常和环境的模型（预测下一个状态的模型）一起被训练。
这种结构允许系统在做出实际决策前，在心理空间中进行前瞻性的规划和评估。
模拟ai英文面试题目及答案
模拟ai英文面试题目及答案模拟AI英文面试题目及答案1. 题目: What is the difference between a neural network anda deep learning model?答案: A neural network is a set of algorithms modeled loosely after the human brain that are designed to recognize patterns. A deep learning model is a neural network with multiple layers, allowing it to learn more complex patterns and features from data.2. 题目: Explain the concept of 'overfitting' in machine learning.答案: Overfitting occurs when a machine learning model learns the training data too well, including its noise and outliers, resulting in poor generalization to new, unseen data.3. 题目: What is the role of a 'bias' in an AI model?答案: Bias in an AI model refers to the systematic errors introduced by the model during the learning process. It can be due to the choice of model, the training data, or the algorithm's assumptions, and it can lead to unfair or inaccurate predictions.4. 题目: Describe the importance of data preprocessing in AI.答案: Data preprocessing is crucial in AI as it involves cleaning, transforming, and reducing the data to a suitableformat for the model to learn effectively. Proper preprocessing can significantly improve the performance of AI models by ensuring that the input data is relevant, accurate, and free from noise.5. 题目: How does reinforcement learning differ from supervised learning?答案: Reinforcement learning is a type of machine learning where an agent learns to make decisions by performing actions in an environment to maximize a reward signal. It differs from supervised learning, where the model learns from labeled data to predict outcomes based on input features.6. 题目: What is the purpose of a 'convolutional neural network' (CNN)?答案: A convolutional neural network (CNN) is a type of deep learning model that is particularly effective for processing data with a grid-like topology, such as images. CNNs use convolutional layers to automatically and adaptively learn spatial hierarchies of features from input images.7. 题目: Explain the concept of 'feature extraction' in AI.答案: Feature extraction in AI is the process of identifying and extracting relevant pieces of information from the raw data. It is a crucial step in many machine learning algorithms, as it helps to reduce the dimensionality of the data and to focus on the most informative aspects that can be used to make predictions or classifications.8. 题目: What is the significance of 'gradient descent' in training AI models?答案: Gradient descent is an optimization algorithm used to minimize a function by iteratively moving in the direction of steepest descent as defined by the negative of the gradient. In the context of AI, it is used to minimize the loss function of a model, thus refining the model's parameters to improve its accuracy.9. 题目: How does 'transfer learning' work in AI?答案: Transfer learning is a technique where a pre-trained model is used as the starting point for learning a new task. It leverages the knowledge gained from one problem to improve performance on a different but related problem, reducing the need for large amounts of labeled data and computational resources.10. 题目: What is the role of 'regularization' in preventing overfitting?答案: Regularization is a technique used to prevent overfitting by adding a penalty term to the loss function, which discourages overly complex models. It helps to control the model's capacity, forcing it to generalize better to new data by not fitting too closely to the training data.。
吴恩达提示词系列解读
吴恩达提示词系列解读在吴恩达的课程、演讲和访谈中，他经常使用一些提示词来帮助学习者更好地理解和应用机器学习和人工智能的概念。
以下是对几个常见提示词的解读，希望能为您带来启发。
1. 拟合曲线（Fitting the curve）：这个概念通常在机器学习中使用，指的是用数学模型去逼近现实世界的数据。
当我们用一个模型拟合一组数据时，我们试图找到一条曲线或函数，以最佳方式描述数据点的分布。
拟合曲线的目标是尽量减小模型与实际数据之间的误差。
2. 正则化（Regularization）：正则化是一种用于防止模型过拟合的技术。
当模型过拟合数据时，它会在训练集上表现得很好，但对新数据的泛化能力较差。
为了减少过拟合的风险，我们可以在模型的损失函数中增加正则化项，使得模型在训练过程中更倾向于学习简单的模式。
3. 梯度消失（Vanishing gradient）：这是一个与深度神经网络相关的问题。
在训练深度神经网络时，反向传播算法计算梯度值，用于更新参数。
然而，当网络很深时，梯度可能逐渐变小，并且在通过每一层传播时几乎消失。
这会导致底层的权重几乎不更新，从而影响模型的学习效果。
解决梯度消失问题的方法之一是使用一些特殊的激活函数，例如ReLU。
4. 数据增强（Data augmentation）：在机器学习中，数据增强是通过对原始数据进行一系列随机变换来扩充训练集的技术。
通过对样本进行旋转、平移、缩放、翻转等操作，可以增加训练数据的多样性，提高模型的泛化能力。
数据增强可以有效地减少过拟合问题。
5. 竞赛驱动（Competition-driven）：竞赛驱动指的是通过参加机器学习竞赛来提高自己的技能和知识。
吴恩达经常鼓励学习者积极参与各种机器学习竞赛，因为这不仅可以锻炼实战能力，还提供了与其他优秀人才交流学习的机会。
竞赛驱动能够更好地推动个人的成长和进步。
以上是对吴恩达常用提示词的解读。
这些提示词涉及了机器学习和人工智能的一些核心概念和方法。
ICML_NIPS_ICCV_CVPR(14~18)
ICML2014ICML20151. An embarrassingly simple approach to zero-shot learning2. Learning Transferable Features with Deep Adaptation Networks3. A Theoretical Analysis of Metric Hypothesis Transfer Learning4. Gradient-based hyperparameter optimization through reversible learningICML20161. One-Shot Generalization in Deep Generative Models2. Meta-Learning with Memory-Augmented Neural Networks3. Meta-gradient boosted decision tree model for weight and target learning4. Asymmetric Multi-task Learning based on Task Relatedness and ConfidenceICML20171. DARLA: Improving Zero-Shot Transfer in Reinforcement Learning2. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks3. Meta Networks4. Learning to learn without gradient descent by gradient descentICML20181. MSplit LBI: Realizing Feature Selection and Dense Estimation Simultaneously in Few-shotand Zero-shot Learning2. Understanding and Simplifying One-Shot Architecture Search3. One-Shot Segmentation in Clutter4. Meta-Learning by Adjusting Priors Based on Extended PAC-Bayes Theory5. Bilevel Programming for Hyperparameter Optimization and Meta-Learning6. Gradient-Based Meta-Learning with Learned Layerwise Metric and Subspace7. Been There, Done That: Meta-Learning with Episodic Recall8. Learning to Explore via Meta-Policy Gradient9. Transfer Learning via Learning to Transfer10. Rapid adaptation with conditionally shifted neuronsNIPS20141. Zero-shot recognition with unreliable attributesNIPS2015NIPS20161. Learning feed-forward one-shot learners2. Matching Networks for One Shot Learning3. Learning from Small Sample Sets by Combining Unsupervised Meta-Training with CNNs NIPS20171. One-Shot Imitation Learning2. Few-Shot Learning Through an Information Retrieval Lens3. Prototypical Networks for Few-shot Learning4. Few-Shot Adversarial Domain Adaptation5. A Meta-Learning Perspective on Cold-Start Recommendations for Items6. Neural Program Meta-InductionNIPS20181. Bayesian Model-Agnostic Meta-Learning2. The Importance of Sampling inMeta-Reinforcement Learning3. MetaAnchor: Learning to Detect Objects with Customized Anchors4. MetaGAN: An Adversarial Approach to Few-Shot Learning5. Regret bounds for meta Bayesian optimization with an unknown Gaussian process prior6. Meta-Gradient Reinforcement Learning7. Meta-Reinforcement Learning of Structured Exploration Strategies8. Meta-Learning MCMC Proposals9. Probabilistic Model-Agnostic Meta-Learning10. MetaReg: Towards Domain Generalization using Meta-Regularization11. Zero-Shot Transfer with Deictic Object-Oriented Representation in Reinforcement Learning12. Uncertainty-Aware Few-Shot Learning with Probabilistic Model-Agnostic Meta-Learning13. Multitask Reinforcement Learning for Zero-shot Generalization with Subtask Dependencies14. Stacked Semantics-Guided Attention Model for Fine-Grained Zero-Shot Learning15. Delta-encoder: an effective sample synthesis method for few-shot object recognition16. One-Shot Unsupervised Cross Domain Translation17. Generalized Zero-Shot Learning with Deep Calibration Network18. Domain-Invariant Projection Learning for Zero-Shot Recognition19. Low-shot Learning via Covariance-Preserving Adversarial Augmentation Network20. Improved few-shot learning with task conditioning and metric scaling21. Adapted Deep Embeddings: A Synthesis of Methods for k-Shot Inductive Transfer Learning22. Learning to Play with Intrinsically-Motivated Self-Aware Agents23. Learning to Teach with Dynamic Loss Functiaons24. Memory Replay GANs: learning to generate images from new categories without forgettingICCV20151. One Shot Learning via Compositions of Meaningful Patches2. Unsupervised Domain Adaptation for Zero-Shot Learning3. Active Transfer Learning With Zero-Shot Priors: Reusing Past Datasets for Future Tasks4. Zero-Shot Learning via Semantic Similarity Embedding5. Semi-Supervised Zero-Shot Classification With Label Representation Learning6. Predicting Deep Zero-Shot Convolutional Neural Networks Using Textual Descriptions7. Learning to Transfer: Transferring Latent Task Structures and Its Application to Person-Specific Facial Action Unit DetectionICCV20171. Supplementary Meta-Learning: Towards a Dynamic Model for Deep Neural Networks2. Attributes2Classname: A Discriminative Model for Attribute-Based Unsupervised Zero-ShotLearning3. Low-Shot Visual Recognition by Shrinking and Hallucinating Features4. Predicting Visual Exemplars of Unseen Classes for Zero-Shot Learning5. Learning Discriminative Latent Attributes for Zero-Shot Classification6. Spatial-Aware Object Embeddings for Zero-Shot Localization and Classification of ActionsCVPR20141. COSTA: Co-Occurrence Statistics for Zero-Shot Classification2. Zero-shot Event Detection using Multi-modal Fusion of Weakly Supervised Concepts3. Learning to Learn, from Transfer Learning to Domain Adaptation: A Unifying Perspective CVPR20151. Zero-Shot Object Recognition by Semantic Manifold DistanceCVPR20162. Multi-Cue Zero-Shot Learning With Strong Supervision3. Latent Embeddings for Zero-Shot Classification4. One-Shot Learning of Scene Locations via Feature Trajectory Transfer5. Less Is More: Zero-Shot Learning From Online Textual Documents With Noise Suppression6. Synthesized Classifiers for Zero-Shot Learning7. Recovering the Missing Link: Predicting Class-Attribute Associations for UnsupervisedZero-Shot Learning8. Fast Zero-Shot Image Tagging9. Zero-Shot Learning via Joint Latent Similarity Embedding10. Learning to Read Chest X-Rays: Recurrent Neural Cascade Model for Automated ImageAnnotation11. Learning to Co-Generate Object Proposals With a Deep Structured Network12. Learning to Select Pre-Trained Deep Representations With Bayesian Evidence Framework13. DeepStereo: Learning to Predict New Views From the World’s ImageryCVPR20171. One-Shot Video Object Segmentation2. FastMask: Segment Multi-Scale Object Candidates in One Shot3. Few-Shot Object Recognition From Machine-Labeled Web Images4. From Zero-Shot Learning to Conventional Supervised Classification: Unseen Visual DataSynthesis5. Learning a Deep Embedding Model for Zero-Shot Learning6. Low-Rank Embedded Ensemble Semantic Dictionary for Zero-Shot Learning7. Multi-Attention Network for One Shot Learning8. Zero-Shot Action Recognition With Error-Correcting Output Codes9. One-Shot Metric Learning for Person Re-Identification10. Semantic Autoencoder for Zero-Shot Learning11. Zero-Shot Recognition Using Dual Visual-Semantic Mapping Paths12. Matrix Tri-Factorization With Manifold Regularizations for Zero-Shot Learning13. One-Shot Hyperspectral Imaging Using Faced Reflectors14. Gaze Embeddings for Zero-Shot Image Classification15. Zero-Shot Learning - the Good, the Bad and the Ugly16. Link the Head to the “Beak”: Zero Shot Learning From Noisy Text Description at PartPrecision17. Semantically Consistent Regularization for Zero-Shot Recognition18. Semantically Consistent Regularization for Zero-Shot Recognition19. Zero-Shot Classification With Discriminative Semantic Representation Learning20. Learning to Detect Salient Objects With Image-Level Supervision21. Quad-Networks: Unsupervised Learning to Rank for Interest Point DetectionCVPR20181. A Generative Adversarial Approach for Zero-Shot Learning From Noisy Texts2. Transductive Unbiased Embedding for Zero-Shot Learning3. Zero-Shot Visual Recognition Using Semantics-Preserving Adversarial EmbeddingNetworks4. Learning to Compare: Relation Network for Few-Shot Learning5. One-Shot Action Localization by Learning Sequence Matching Network6. Multi-Label Zero-Shot Learning With Structured Knowledge Graphs7. “Zero-Shot” Super-Resolution Using Deep Internal Learning8. Low-Shot Learning With Large-Scale Diffusion9. CLEAR: Cumulative LEARning for One-Shot One-Class Image Recognition10. Zero-Shot Sketch-Image Hashing11. Structured Set Matching Networks for One-Shot Part Labeling12. Memory Matching Networks for One-Shot Image Recognition13. Generalized Zero-Shot Learning via Synthesized Examples14. Dynamic Few-Shot Visual Learning Without Forgetting15. Exploit the Unknown Gradually: One-Shot Video-Based Person Re-Identification byStepwise Learning16. Feature Generating Networks for Zero-Shot Learning17. Low-Shot Learning With Imprinted Weights18. Zero-Shot Recognition via Semantic Embeddings and Knowledge Graphs19. Webly Supervised Learning Meets Zero-Shot Learning: A Hybrid Approach for Fine-Grained Classification20. Few-Shot Image Recognition by Predicting Parameters From Activations21. Low-Shot Learning From Imaginary Data22. Discriminative Learning of Latent Features for Zero-Shot Recognition23. Multi-Content GAN for Few-Shot Font Style Transfer24. Preserving Semantic Relations for Zero-Shot Learning25. Zero-Shot Kernel Learning26. Neural Style Transfer via Meta Networks27. Learning to Estimate 3D Human Pose and Shape From a Single Color Image28. Learning to Segment Every Thing29. Leveraging Unlabeled Data for Crowd Counting by Learning to Rank。
自动归因算法
自动归因算法
自动归因算法是一种基于梯度的归因算法，该算法普遍认为神经网络的输出对每个输入单元的梯度可以反映输入单元的重要性。
以下是该算法的一种解释：
自动归因算法会将输入单元的重要性建模为梯度与输入单元值的逐元素乘积。
梯度仅能反映输入单元的局部重要性，而平滑梯度和集成梯度算法将重要性建模为平均梯度与输入单元值的逐元素乘积，其中这两种方法中的平均梯度分别指输入样本邻域内梯度的平均值或输入样本到基准点间线性插值点的梯度平均值。
类似地，Grad-CAM算法采用网络输出对每个channel中所有特征梯度的平均值，来计算重要性分数。
进一步，Expected Gradients算法认为，选择单个基准点往往会导致有偏的归因结果，从而提出将重要性建模为不同基准点下的集成梯度。
以上内容仅供参考，建议查阅关于自动归因算法的资料获取更全面的信息。
基于机器学习的运动跟踪算法研究
基于机器学习的运动跟踪算法研究近年来，运动跟踪技术越来越受到人们的关注，这项技术可以通过感知人体的动作和位置信息，从而对其进行有效地监测和预测，为运动员、健身爱好者、医疗行业等带来了很多好处。
随着人工智能的飞速发展，基于机器学习的运动跟踪算法研究也成为了当前的热门话题之一。
在传统的运动跟踪系统中，往往需要运用相机、传感器等硬件设备进行人体姿态的检测，虽然这些设备可以获得高精度的位置和运动信息，但是在实际场景中使用却十分困难。
例如，这些硬件设备需要高昂的费用、需要定期维护和校准、灵活性不够等等。
而基于机器学习的运动跟踪算法则可以通过运用人工智能的方法来精确掌握人体运动的轨迹，并且可以在不同的软硬件平台上运行。
在进行运动跟踪的过程中，机器学习算法可以帮助我们处理大量的运动数据，并且自动提取特征信息，这些特征信息通常包括关节角度、身体方位、速度等。
同时，基于机器学习的算法可以通过学习大量真实的运动数据，建立强大的模型，从而提高运动跟踪的精度和稳定性。
在运动跟踪算法的研究中，最常用的方法是三维姿态估计，这项技术可以将人体的节点关键点定位到具体的三维坐标系中，从而精确描述身体的姿态变化。
然而，传统的三维姿态估算方法通常需要计算庞大的矩阵运算和优化，计算量巨大，难以满足实时性要求。
近年来，研究者们结合机器学习技术推出了“一阶模型”和“二阶模型”，旨在减小计算量和提升实时性。
在一阶模型中，通过深度学习网络学习到具有时空特性的运动特征，然后再使用简单的线性模型进行估计，以提高计算效率。
而二阶模型则通过人体运动的加速度和角速度信息，进一步提高了姿态估计的精度和实时性。
此外，基于机器学习的运动跟踪算法还具有很强的适应性。
根据实际情况的不同，我们可以选择不同的算法和模型，来适应不同的运动项目和运动员个体的特点。
例如，在足球比赛中，为了更好地跟踪球员的位置和行踪路线，研究者们选择使用多目标跟踪算法。
这是一种基于卡尔曼滤波和粒子滤波的算法，大大提升了准确度和实时性，同时还能对球员的姿态和动作进行实时监测。
A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection
A Study of Cross-Validation and Bootstrap for Accuracy E stimation and Model SelectionRon KohaviComputer Science DepartmentStanford UniversityStanford, CA 94305ronnykGCS Stanford E D Uh t t p //r o b o t i c s Stanford edu/"ronnykA b s t r a c tWe review accuracy estimation methods andcompare the two most common methods cross-validation and bootstrap Recent experimen-tal results on artificial data and theoretical recults m restricted settings have shown that forselecting a good classifier from a set of classi-fiers (model selection), ten-fold cross-validationmay be better than the more expensive ka\pone-out cross-validation We report on a large-scale experiment—over half a million runs ofC4 5 and aNaive-Bayes algorithm—loestimalethe effects of different parameters on these algonthms on real-world datascts For cross-validation we vary the number of folds andwhether the folds arc stratified or not, for boot-strap, we vary the number of bootstrap sam-ples Our results indicate that for real-worddatasets similar to ours, The best method lo usefor model selection is ten fold stratified crossvalidation even if computation power allowsusing more folds1 I n t r o d u c t i o nIt can not be emphasized enough that no claimwhatsoever 11 being made in this paper that altalgorithms a re equiva lent in practice in the rea l world In pa rticula r no cla im is being ma de tha t ont should not use cross va lida tion in the real world— Wolpcrt (1994a.) Estimating the accuracy of a classifier induced by su-pervised learning algorithms is important not only to predict its future prediction accuracy, but also for choos-ing a classifier from a given set (model selection), or combining classifiers (Wolpert 1992) For estimating the final accuracy of a classifier, we would like an estimation method with low bias and low variance To choose a classifier or to combine classifiers, the absolute accura-cies are less important and we are willing to trade off biasA longer version of the paper can be retrieved by anony mous ftp to starry Htanford edu pub/ronnyk/accEst-long ps for low variance, assuming the bias affects all classifiers similarly (e g esLimates are ")% pessimistic)In this paper we explain some of the assumptions madeby Ihe different estimation methods and present con-crete examples where each method fails While it is known that no accuracy estimation can be corrert allthe time (Wolpert 1994b Schaffer 1994j we are inter ested in identifying a method that ib well suited for the biases and tn rids in typical real world datasetsRecent results both theoretical and experimental, have shown that it is no! alwa>s the case that increas-ing the computational cost is beneficial especiallhy if the relative accuracies are more important than the exact values For example leave-one-out is almost unbiased,but it has high variance leading to unreliable estimates (Efron 1981) l o r linear models using leave-one-out cross-validation for model selection is asymptotically in consistent in the sense that the probability of selectingthe model with the best predictive power does not con-verge to one as the lolal number of observations ap-proaches infinity (Zhang 1992, Shao 1993)This paper \s organized AS follows Section 2 describesthe common accuracy estimation methods and ways of computing confidence bounds that hold under some as-sumptions Section 3 discusses related work comparing cross-validation variants and bootstrap variants Sec lion 4 discusses methodology underlying our experimentThe results of the experiments are given Section 5 with a discussion of important observations We conelude witha summary in Section 62 Methods for Accuracy E s t i m a t i o nA classifier is a function that maps an unlabelled in-stance to a label using internal data structures An i n-ducer or an induction algorithm builds a classifier froma given dataset CART and C 4 5 (Brennan, Friedman Olshen &. Stone 1984, Quinlan 1993) are decision tree in-ducers that build decision tree classifiers In this paperwe are not interested in the specific method for inducing classifiers, but assume access to a dataset and an inducerof interestLet V be the space of unlabelled instances and y theKOHAVI 1137set of possible labels be the space of labelled instances and ,i n ) be a dataset (possibly a multiset) consisting of n labelled instances, where A classifier C maps an unla-beled instance ' 10 a l a b e l a n d an inducer maps a given dataset D into a classifier CThe notationwill denote the label assigned to an unlabelled in-stance v by the classifier built, by inducer X on dataset D tWe assume that there exists adistribution on the set of labelled instances and that our dataset consists of 1 1 d (independently and identically distributed) instances We consider equal misclassifica-lion costs using a 0/1 loss function, but the accuracy estimation methods can easily be extended to other loss functionsThe accuracy of a classifier C is the probability ofcorrectly clasaifying a randoml\ selected instance, i efor a randomly selected instancewhere the probability distribution over theinstance space 15 the same as the distribution that was used to select instances for the inducers training set Given a finite dataset we would like to custimate the fu-ture performance of a classifier induced by the given in-ducer and dataset A single accuracy estimate is usually meaningless without a confidence interval, thus we will consider how to approximate such an interval when pos-sible In order to identify weaknesses, we also attempt o identify cases where the estimates fail2 1 Holdout The holdout method sometimes called test sample esti-mation partitions the data into two mutually exclusivesubsets called a training set and a test set or holdout setIt is Lommon to designate 2/ 3 of the data as the trainingset and the remaining 1/3 as the test set The trainingset is given to the inducer, and the induced classifier istested on the test set Formally, let , the holdout set,be a subset of D of size h, and let Theholdout estimated accuracy is defined aswhere otherwise Assummg that the inducer s accuracy increases as more instances are seen, the holdout method is a pessimistic estimator because only a portion of the data is given to the inducer for training The more instances we leave for the test set, the higher the bias of our estimate however, fewer test set instances means that the confidence interval for the accuracy will be wider as shown belowEach test instance can be viewed as a Bernoulli trialcorrect or incorrect prediction Let S be the numberof correct classifications on the test set, then s is dis-tributed bmomially (sum of Bernoulli trials) For rea-sonably large holdout sets, the distribution of S/h is ap-proximately normal with mean ace (the true accuracy of the classifier) and a variance of ace * (1 — acc)hi Thus, by De Moivre-Laplace limit theorem, we havewhere z is the quanl lie point of the standard normal distribution To get a IOO7 percent confidence interval, one determines 2 and inverts the inequalities Inversion of the inequalities leads to a quadratic equation in ace, the roots of which are the low and high confidence pointsThe above equation is not conditioned on the dataset D , if more information is available about the probability of the given dataset it must be taken into accountThe holdout estimate is a random number that de-pends on the division into a training set and a test set In r a n d o m sub s a m p l i n g the holdout method is re-peated k times and the eslimated accuracy is derived by averaging the runs Th( slandard deviation can be estimated as the standard dewation of the accuracy es-timations from each holdout runThe mam assumption that is violated in random sub-sampling is the independence of instances m the test set from those in the training set If the training and testset are formed by a split of an original dalaset, thenan over-represented class in one subset will be a under represented in the other To demonstrate the issue we simulated a 2/3, 1 /3 split of Fisher's famous ins dataset and used a majority inducer that builds a classifier pre dieting the prevalent class in the training set The iris dataset describes ins plants using four continuous fea-tures, and the task is to classify each instance (an ins) as Ins Setosa Ins Versicolour or Ins Virginica For each class label there are exactly one third of the instances with that label (50 instances of each class from a to-tal of 150 instances) thus we expect 33 3% prediction accuracy However, because the test set will always con-tain less than 1/3 of the instances of the class that wasprevalent in the training set, the accuracy predicted by the holdout method is 21 68% with a standard deviation of 0 13% (estimated by averaging 500 holdouts) In practice, the dataset size is always finite, and usu-ally smaller than we would like it to be The holdout method makes inefficient use of the data a third of dataset is not used for training the inducer 2 2 Cross-Validation, Leave-one-out, and Stratification In fc-fold cross-validation, sometimes called rotation esti-mation, the dataset V is randomly split into k mutuallyexclusive subsets (the folds) , of approx-imately equal size The inducer is trained and tested1138 LEARNINGThe cross-validation estimate is a random number that depends on the division into folds C o m p l e t ec r o s s -v a l id a t i o n is the average of all possibil ities for choosing m/k instances out of m, but it is usually too expensive Exrept for leave-one-one (rc-fold cross-validation), which is always complete, fc-foM cross-validation is estimating complete K-foId cross-validationusing a single split of the data into the folds Repeat-ing cross-validation multiple limes using different spillsinto folds provides a better M onte C arlo estimate to 1 hecomplele cross-validation at an added cost In s t r a t i -fied c r o s s -v a l i d a t i o n the folds are stratified so thaitlicy contain approximately the same proportions of la-bels as the original dataset An inducer is stable for a given dataset and a set of perturbal ions if it induces classifiers thai make the same predictions when it is given the perturbed datasets P r o p o s i t i o n 1 (V a r i a n c e in A>fold C V )Given a dataset and an inducer If the inductr isstable under the pei tur bations caused by deleting theinstances f o r thr folds in k fold cross-validatwn thecross validation < stnnate will be unbiastd and the t a i lance of the estimated accuracy will be approximatelyaccrv (1—)/n when n is the number of instancesin the datasi t Proof If we assume that the k classifiers produced makethe same predictions, then the estimated accuracy has a binomial distribution with n trials and probabihly of success equal to (he accuracy of the classifier | For large enough n a confidence interval may be com-puted using Equation 3 with h equal to n, the number of instancesIn reality a complex inducer is unlikely to be stable for large perturbations unless it has reached its maximal learning capacity We expect the perturbations induced by leave-one-out to be small and therefore the classifier should be very stable As we increase the size of the perturbations, stability is less likely to hold we expect stability to hold more in 20-fold cross-validation than in 10-fold cross-validation and both should be more stable than holdout of 1/3 The proposition does not apply to the resubstitution estimate because it requires the in-ducer to be stable when no instances are given in the datasetThe above proposition helps, understand one possible assumption that is made when using cross-validation if an inducer is unstable for a particular dataset under a set of perturbations introduced by cross-validation, the ac-curacy estimate is likely to be unreliable If the inducer is almost stable on a given dataset, we should expect a reliable estimate The next corollary takes the idea slightly further and shows a result that we have observed empirically there is almost no change in the variance of the cross validation estimate when the number of folds is variedC o r o l l a r y 2 (Variance m cross-validation)Given a dataset and an inductr If the inducer is sta-ble undfi the }>tituibuhoris (aused by deleting the test instances foi the folds in k-fold cross-validation for var-ious valuts of k then tht vartanct of the estimates will be the sameProof The variance of A-fold cross-validation in Propo-sition 1 does not depend on k |While some inducers are liktly to be inherently more stable the following example shows that one must also take into account the dalaset and the actual perturba (ions E x a m p l e 1 (Failure of leave-one-out)lusher s ins dataset contains 50 instances of each class leading one to expect that a majority indu<er should have acruraov about j \% However the eombmation ofthis dataset with a majority inducer is unstable for thesmall perturbations performed by leave-one-out Whenan instance is deleted from the dalaset, its label is a mi-nority in the training set, thus the majority inducer pre-dicts one of the other two classes and always errs in clas-sifying the test instance The leave-one-out estimatedaccuracy for a majont> inducer on the ins dataset istherefore 0% M oreover all folds have this estimated ac-curacy, thus the standard deviation of the folds is again0 %giving the unjustified assurance that 'he estimate is stable | The example shows an inherent problem with cross-validation th-t applies to more than just a majority in-ducer In a no-infornirition dataset where the label val-ues are completely random, the best an induction algo-rithm can do is predict majority Leave-one-out on such a dataset with 50% of the labels for each class and a majontv ind'-cer (the best, possible inducer) would still predict 0% accuracy 2 3 B o o t s t r a pThe bootstrap family was introduced by Efron and is fully described in Efron &. Tibshirani (1993) Given a dataset of size n a b o o t s t r a p s a m p l e is created by sampling n instances uniformly from the data (with re-placement) Since the dataset is sampled with replace-ment, the probability of any given instance not beingchosen after n samples is theKOHAVI 1139expected number of distinct instances from the original dataset appearing in the teat set is thus 0 632n The eO accuracy estimate is derived by using the bootstrap sam-ple for training and the rest of the instances for testing Given a number b, the number of bootstrap samples, let e0, be the accuracy estimate for bootstrap sample i The632 bootstrap estimate is defined as(5)where ace, is the resubstitution accuracy estimate on the full dataset (i e , the accuracy on the training set) The variance of the estimate can be determined by com puting the variance of the estimates for the samples The assumptions made by bootstrap are basically the same as that of cross-validation, i e , stability of the al-gorithm on the dataset the 'bootstrap world" should closely approximate the real world The b32 bootstrap fails (o give the expected result when the classifier is a perfect memonzer (e g an unpruned decision tree or a one nearest neighbor classifier) and the dataset is com-pletely random, say with two classes The resubstitution accuracy is 100%, and the eO accuracy is about 50% Plugging these into the bootstrap formula, one gets an estimated accuracy of about 68 4%, far from the real ac-curacy of 50% Bootstrap can be shown to fail if we add a memonzer module to any given inducer and adjust its predictions If the memonzer remembers the training set and makes the predictions when the test instance was a training instances, adjusting its predictions can make the resubstitution accuracy change from 0% to 100% and can thus bias the overall estimated accuracy in any direction we want3 Related W o r kSome experimental studies comparing different accuracy estimation methods have been previously done but most of them were on artificial or small datasets We now describe some of these effortsEfron (1983) conducted five sampling experiments and compared leave-one-out cross-validation, several variants of bootstrap, and several other methods The purpose of the experiments was to 'investigate some related es-timators, which seem to offer considerably improved es-timation in small samples ' The results indicate that leave-one-out cross-validation gives nearly unbiased esti-mates of the accuracy, but often with unacceptably high variability, particularly for small samples, and that the 632 bootstrap performed bestBreiman et al (1984) conducted experiments using cross-validation for decision tree pruning They chose ten-fold cross-validation for the CART program and claimed it was satisfactory for choosing the correct tree They claimed that "the difference in the cross-validation estimates of the risks of two rules tends to be much more accurate than the two estimates themselves "Jain, Dubes fa Chen (1987) compared the performance of the t0 bootstrap and leave-one-out cross-validation on nearest neighbor classifiers Using artificial data and claimed that the confidence interval of the bootstrap estimator is smaller than that of leave-one-out Weiss (1991) followed similar lines and compared stratified cross-validation and two bootstrap methods with near-est neighbor classifiers His results were that stratified two-fold cross validation is relatively low variance and superior to leave-one-outBreiman fa Spector (1992) conducted a feature sub-set selection experiments for regression, and compared leave-one-out cross-validation, A:-fold cross-validation for various k, stratified K-fold cross-validation, bias-corrected bootstrap, and partial cross-validation (not discussed here) Tests were done on artificial datasets with 60 and 160 instances The behavior observed was (1) the leave-one-out has low bias and RMS (root mean square) error whereas two-fold and five-fold cross-validation have larger bias and RMS error only at models with many features, (2) the pessimistic bias of ten-fold cross-validation at small samples was significantly re-duced for the samples of size 160 (3) for model selection, ten-fold cross-validation is better than leave-one-out Bailey fa E lkan (1993) compared leave-one-out cross-ahdation to 632 bootstrap using the FOIL inducer and four synthetic datasets involving Boolean concepts They observed high variability and little bias in the leave-one-out estimates, and low variability but large bias in the 632 estimatesWeiss and Indurkyha (Weiss fa Indurkhya 1994) con-ducted experiments on real world data Lo determine the applicability of cross-validation to decision tree pruning Their results were that for samples at least of size 200 using stratified ten-fold cross-validation to choose the amount of pruning yields unbiased trees (with respect to their optimal size) 4 M e t h o d o l o g yIn order to conduct a large-scale experiment we decided to use 04 5 and a Naive Bayesian classifier The C4 5 algorithm (Quinlan 1993) is a descendent of ID3 that builds decision trees top-down The Naive-Bayesian clas-sifier (Langley, Iba fa Thompson 1992) used was the one implemented in (Kohavi, John, Long, Manley fa Pfleger 1994) that uses the observed ratios for nominal features and assumes a Gaussian distribution for contin-uous features The exact details are not crucial for this paper because we are interested in the behavior of the accuracy estimation methods more than the internals of the induction algorithms The underlying hypothe-sis spaces—decision trees for C4 5 and summary statis-tics for Naive-Bayes—are different enough that we hope conclusions based on these two induction algorithms will apply to other induction algorithmsBecause the target concept is unknown for real-world1140 LEARNINGconcepts, we used the holdout method to estimate the quality of the cross-validation and bootstrap estimates To choose & set of datasets, we looked at the learning curves for C4 5 and Najve-Bayes for most of the super-vised classification dataaets at the UC Irvine repository (Murphy & Aha 1994) that contained more than 500 instances (about 25 such datasets) We felt that a min-imum of 500 instances were required for testing While the true accuracies of a real dataset cannot be computed because we do not know the target concept, we can esti mate the true accuracies using the holdout method The "true' accuracy estimates in Table 1 were computed by taking a random sample of the given size computing the accuracy using the rest of the dataset as a test set, and repeating 500 timesWe chose six datasets from a wide variety of domains, such that the learning curve for both algorithms did not flatten out too early that is, before one hundred instances We also added a no inform a tion d l stt, rand, with 20 Boolean features and a Boolean random label On one dataset vehicle, the generalization accu-racy of the Naive-Bayes algorithm deteriorated hy morethan 4% as more instances were g;iven A similar phenomenon was observed on the shuttle dataset Such a phenomenon was predicted by Srhaffer and Wolpert (Schaffer 1994, Wolpert 1994), but we were surprised that it was observed on two real world datasetsTo see how well an Accuracy estimation method per forms we sampled instances from the dataset (uniformly without replacement) and created a training set of the desired size We then ran the induction algorihm on the training set and tested the classifier on the rest of the instances L E I the dataset This was repeated 50 times at points where the lea rning curve wa s sloping up The same folds in cross-validation and the same samples m bootstrap were used for both algorithms compared5 Results and DiscussionWe now show the experimental results and discuss their significance We begin with a discussion of the bias in the estimation methods and follow with a discussion of the variance Due to lack of space, we omit some graphs for the Naive-Bayes algorithm when the behavior is ap-proximately the same as that of C 4 5 5 1 T h e B i a sThe bias of a method to estimate a parameter 0 is de-fined as the expected value minus the estimated value An unbiased estimation method is a method that has zero bias Figure 1 shows the bias and variance of k-fold cross-validation on several datasets (the breast cancer dataset is not shown)The diagrams clearly show that k-fold cross-validation is pessimistically biased, especially for two and five folds For the learning curves that have a large derivative at the measurement point the pessimism in k-fold cross-Figure ] C'4 5 The bias of cross-validation with varying folds A negative K folds stands for leave k-out E rror bars are 95% confidence intervals for (he mean The gray regions indicate 95 % confidence intervals for the true ac curaries Note the different ranges for the accuracy axis validation for small k s is apparent Most of the esti-mates are reasonably good at 10 folds and at 20 folds they art almost unbiasedStratified cross validation (not shown) had similar be-havior, except for lower pessimism The estimated accu-racy for soybe an at 2 fold was 7% higher and at five-fold, 1 1% higher for vehicle at 2-fold, the accuracy was 2 8% higher and at five-fold 1 9% higher Thus stratification seems to be a less biased estimation methodFigure 2 shows the bias and variance for the b32 boot-strap accuracy estimation method Although the 632 bootstrap is almost unbiased for chess hypothyroid, and mushroom for both inducers it is highly biased for soy-bean with C'A 5, vehicle with both inducers and rand with both inducers The bias with C4 5 and vehicle is 9 8%5 2 The VarianceWhile a given method may have low bias, its perfor-mance (accuracy estimation in our case) may be poor due to high variance In the experiments above, we have formed confidence intervals by using the standard de-viation of the mea n a ccura cy We now switch to the standard deviation of the population i e , the expected standard deviation of a single accuracy estimation run In practice, if one dots a single cross-validation run the expected accuracy will be the mean reported above, but the standard deviation will be higher by a factor of V50, the number of runs we averaged in the experimentsKOHAVI 1141Table 1 True accuracy estimates for the datasets using C4 5 and Naive-Bayes classifiers at the chosen sample sizesFigure 2 C4 5 The bias of bootstrap with varying sam-ples Estimates are good for mushroom hypothyroid, and chess, but are extremely biased (optimistically) for vehicle and rand, and somewhat biased for soybeanIn what follows, all figures for standard deviation will be drawn with the same range for the standard devi-ation 0 to 7 5% Figure 3 shows the standard devia-tions for C4 5 and Naive Bayes using varying number of folds for cross-validation The results for stratified cross-validation were similar with slightly lower variance Figure 4 shows the same information for 632 bootstrap Cross-validation has high variance at 2-folds on both C4 5 and Naive-Bayes On C4 5, there is high variance at the high-ends too—at leave-one-out and leave-two-out—for three files out of the seven datasets Stratifica-tion reduces the variance slightly, and thus seems to be uniformly better than cross-validation, both for bias and vananceFigure 3 Cross-validation standard deviation of accu-racy (population) Different, line styles are used to help differentiate between curves6 S u m m a r yWe reviewed common accuracy estimation methods in-cluding holdout, cross-validation, and bootstrap, and showed examples where each one fails to produce a good estimate We have compared the latter two approaches on a variety of real-world datasets with differing charac-teristicsProposition 1 shows that if the induction algorithm is stable for a given dataset, the variance of the cross-validation estimates should be approximately the same, independent of the number of folds Although the induc-tion algorithms are not stable, they are approximately stable it fold cross-validation with moderate k values (10-20) reduces the variance while increasing the bias As k decreases (2-5) and the sample sizes get smaller, there is variance due to the instability of the training1142 LEARNING1 igure 4 632 Bootstrap standard deviation in acc-rat y (population)sets themselves leading to an increase in variance this is most apparent for datasets with many categories, such as soybean In these situations) stratification seems to help, but -epeated runs may be a better approach Our results indicate that stratification is generally a better scheme both in terms of bias and variance whencompared to regular cross-validation Bootstrap has low,variance but extremely large bias on some problems We recommend using stratified Len fold cross-validation for model selection A c k n o w l e d g m e n t s We thank David Wolpert for a thorough reading of this paper and many interesting dis-cussions We thank Tom Bylander Brad E fron Jerry Friedman, Rob Holte George John Pat Langley Hob Tibshiram and Sholom Weiss for their helpful com nients and suggestions Dan Sommcrfield implemented Lhe bootstrap method in WLC++ All experiments were conducted using M L C ++ partly partly funded by ONR grant N00014-94-1-0448 and NSF grants IRI 9116399 and IRI-941306ReferencesBailey, T L & E lkan C (1993) stimating the atcuracy of learned concepts, in Proceedings of In ternational Joint Conference on Artificial Intelli-gence , Morgan Kaufmann Publishers, pp 895 900 Breiman, L & Spector, P (1992) Submodel selectionand evaluation in regression the x random case Inttrnational St atistic al Review 60(3), 291-319 Breiman, L , Friedman, J H , Olshen, R A & StoneC J (1984), Cl a ssific ation a nd Regression Trets Wadsworth International GroupEfron, B (1983), 'E stimating the error rate of a pre-diction rule improvement on cross-validation",Journal of the Americ an St atistic al Associ ation 78(382), 316-330 Efron, B & Tibshiram, R (1993) An introduction tothe bootstra p, Chapman & HallJam, A K Dubes R C & Chen, C (1987), "Boot-strap techniques lor error estimation", IEEE tra ns-actions on p a ttern a n a lysis a nd m a chine intelli-gence P A M I -9(5), 628-633 Kohavi, R , John, G , Long, R , Manley, D &Pfleger K (1994), M L C ++ A machine learn-ing library in C ++ in 'Tools with Artifi-cial Intelligence I E E EComputer Society Press, pp 740-743 Available by anonymous ftp from s t a r r y Stanford E DU pub/ronnyk/mlc/ toolsmlc psLangley, P Tba, W & Thompson, K (1992), An anal-ysis of bayesian classifiers in Proceedings of the tenth national conference on artificial intelligence",A A A I Press and M I T Press, pp 223-228Murph' P M & Aha D W (1994), V( I repository of machine learning databases, For information con-tact ml-repository (Ui(,s uci edu Quinlan I R (1993) C4 5 Progra ms for Ma chine Learning Morgan Kaufmann Los Altos CaliforniaSchaffcr C (19941 A conservation law for generalization performance, in Maehinc Learning Proceedings of Lhe E leventh International conference Morgan Kaufmann, pp 259-265Shao, J (1993), Linear model seletion via cross-validation Journ a l of the America n sta tistica l As-sociation 88(422) 486-494 Weiss S M (1991), Small sample error rate estimationfor k nearest neighbor classifiers' I E EE Tr a ns ac tions on Pa ttern An alysis a nd Ma chine Intelligence 13(3), 285-289 Weiss, S M & lndurkhya N (1994) Decision Lreepruning Biased or optimal, in Proceedings of the twelfth national conference on artificial intel-ligence A A A I Press and M I T Press pp 626-632 Wolpert D H (1992), Stacked generalization , Neura lNetworks 5 241-259 Wolpert D H (1994a) Off training set error and a pri-ori distinctions between learning algorithms, tech-mcal Report SFI TR 94-12-121, The Sante Fe ln-stituteWolpert D II {1994b), The relationship between PAC, the statistical physics framework the Bayesian framework, and the VC framework Technical re-port, The Santa Fe Institute Santa Fe, NMZhang, P (1992), 'On the distributional properties of model selection criteria' Journ al of the America nStatistical Associa tion 87(419), 732-737 KOHAVI 1143。
半监督学习中的自训练方法详解(Ⅰ)
半监督学习中的自训练方法详解在机器学习领域，半监督学习是一种重要的学习范式，它利用大量的无标签数据来提高模型的性能。
在半监督学习中，自训练（self-training）方法是一种常见的技术，它通过将模型预测的标签作为伪标签来训练模型，从而利用无标签数据进行学习。
本文将详细介绍自训练方法在半监督学习中的应用。
自训练方法的基本原理是利用模型对无标签数据进行预测，并将预测的标签作为真实标签进行训练。
具体来说，自训练方法首先使用有标签数据对模型进行初始化训练，然后利用模型对无标签数据进行预测，将预测标签置信度较高的样本作为伪标签，将这些伪标签与有标签数据合并，重新训练模型。
这个过程迭代进行，直到收敛为止。
在实际应用中，自训练方法需要解决一些关键问题。
首先是伪标签的可靠性和准确性。
因为伪标签是模型预测的结果，其准确性不如有标签数据。
因此，自训练方法需要设计一些策略来筛选和修正伪标签，以减少错误标注对模型训练的影响。
其次是训练样本的平衡性。
在自训练过程中，模型可能会产生偏向某些类别的情况，导致模型性能下降。
因此，需要设计合适的样本选择策略来保持训练样本的平衡性。
最后是训练过程的收敛性。
自训练方法的迭代训练过程需要一些调控策略来保证模型收敛。
针对上述问题，研究者们提出了许多改进自训练方法的技术。
其中，伪标签的可靠性可以通过置信度阈值来筛选高置信度的伪标签，通过集成学习方法来修正伪标签。
训练样本的平衡性可以通过引入样本选择策略，比如在每轮训练中保持不同类别的样本比例。
训练过程的收敛性可以通过引入早停策略，比如监控验证集的性能，当性能不再提升时停止训练。
除了上述改进技术，自训练方法还可以与其他半监督学习方法结合，以提高模型性能。
比如，自训练方法可以与生成对抗网络（GAN）结合，利用生成对抗网络生成的数据来增强模型的泛化能力。
自训练方法还可以与图卷积网络（GCN）结合，利用图结构信息来进行自训练。
在实际应用中，自训练方法已经取得了许多成功的应用。
causal 和 autoregressive language model
Causal and Autoregressive Language Model: A Comprehensive GuideLanguage models have been the focus of attention in the field of Natural Language Processing (NLP) for several years. These models enable computers to understand and generate human language, facilitating various text-based applications like speech recognition, machine translation, and text summarization. With the advent of deep learning, language models have seen a significant boost in their performance and accuracy, leading to the development of two popular models - Causal and Autoregressive Language Model.Causal Language ModelCausal Language Model is a type of language model that can predict the next word in a sentence given the previous words. In simpler terms, it is a left-to-right model that uses a sequence of past words to predict the next word. The model generates a probability distribution over the next word in the vocabulary, and the word with the highest probability is selected as the predicted word. The causal language model is also used in predicting the next sentence in a paragraph or document.One of the most popular implementations of the causal language model is the GPT-2 (Generative Pre-trained Transformer 2) model by OpenAI. GPT-2 is a transformer-based model with 1.5 billion parameters, which makes it one of the most potent language models in the world. GPT-2 uses causal language modeling to generate coherent and relevant text that mimics human-like writing styles.Autoregressive Language ModelAutoregressive language models are another type of language models that use the current word to predict the next word in a sentence. Unlike causal language models, autoregressive models use a right-to-left approach, where the current word is dependent on future words. These models use an autoregressive process to generate text, where every predicted word is conditioned on a set of previously predicted words.The autoregressive model is a type of Recurrent Neural Network (RNN), which is trained on a large corpus of text data using a back-propagation algorithm. During training, the model learns to predict the most probable next word in a sequence based on the conditional probability of the previous words.One of the most popular implementations of the autoregressive language model is the LSTM (Long Short-Term Memory) model, which has shown excellent performance in various applications, including speech recognition, machine translation, and text classification tasks.Differences between Causal and Autoregressive Language ModelsThe significant difference between Causal and Autoregressive Language Models is the direction in which the model operates. Causal language models use a left-to-right approach, where the current prediction is dependent only on the previous words, while autoregressive models use a right-to-left approach, where the current prediction is dependent on future words.In causal language models, predictions are made sequentially, word-by-word, using a self-attention mechanism, which learns the relationship between the current word and the previous words. In contrast, the autoregressive model attempts to maintain the coherence and context of the entire sentence using a recursive architecture.Another difference between these two models is the way they are trained. Causal language models are trained in an unconditional setting, where the model does not have any specific task to perform, while autoregressive models are trained in a conditional setting, where the model is given a specific task to perform, such as machine translation or speech recognition.ConclusionCausal and Autoregressive Language Models are significant advancements in language modeling that have opened up new avenues for research in NLP. These models have shown tremendous performance in various tasks like speech recognition, machine translation, summarization, and text classification. While both models use different approaches, they are both essential in building state-of-the-art language models. As research in language models continues to progress, we can expect more advancements in the field of NLP that will enable computers to understand and generate human-like language in more complex and meaningful ways.。
深度强化学习在自动驾驶中的应用研究(英文中文双语版优质文档)
深度强化学习在自动驾驶中的应用研究（英文中文双语版优质文档）Application Research of Deep Reinforcement Learning in Autonomous DrivingWith the continuous development and progress of artificial intelligence technology, autonomous driving technology has become one of the research hotspots in the field of intelligent transportation. In the research of autonomous driving technology, deep reinforcement learning, as an emerging artificial intelligence technology, is increasingly widely used in the field of autonomous driving. This paper will explore the application research of deep reinforcement learning in autonomous driving.1. Introduction to Deep Reinforcement LearningDeep reinforcement learning is a machine learning method based on reinforcement learning, which enables machines to intelligently acquire knowledge and experience from the external environment, so that they can better complete tasks. The basic framework of deep reinforcement learning is to use the deep learning network to learn the mapping of state and action. Through continuous interaction with the environment, the machine can learn the optimal strategy, thereby realizing the automation of tasks.The application of deep reinforcement learning in the field of automatic driving is to realize the automation of driving decisions through machine learning, so as to realize intelligent driving.2. Application of Deep Reinforcement Learning in Autonomous Driving1. State recognition in autonomous drivingIn autonomous driving, state recognition is a very critical step, which mainly obtains the state information of the environment through sensors and converts it into data that the computer can understand. Traditional state recognition methods are mainly based on rules and feature engineering, but this method not only requires human participation, but also has low accuracy for complex environmental state recognition. Therefore, the state recognition method based on deep learning has gradually become the mainstream method in automatic driving.The deep learning network can perform feature extraction and classification recognition on images and videos collected by sensors through methods such as convolutional neural networks, thereby realizing state recognition for complex environments.2. Decision making in autonomous drivingDecision making in autonomous driving refers to the process of formulating an optimal driving strategy based on the state information acquired by sensors, as well as the goals and constraints of the driving task. In deep reinforcement learning, machines can learn optimal strategies by interacting with the environment, enabling decision making in autonomous driving.The decision-making process of deep reinforcement learning mainly includes two aspects: one is the learning of the state-value function, which is used to evaluate the value of the current state; the other is the learning of the policy function, which is used to select the optimal action. In deep reinforcement learning, the machine can learn the state-value function and policy function through the interaction with the environment, so as to realize the automation of driving decision-making.3. Behavior Planning in Autonomous DrivingBehavior planning in autonomous driving refers to selecting an optimal behavior from all possible behaviors based on the current state information and the goal of the driving task. In deep reinforcement learning, machines can learn optimal strategies for behavior planning in autonomous driving.4. Path Planning in Autonomous DrivingPath planning in autonomous driving refers to selecting the optimal driving path according to the goals and constraints of the driving task. In deep reinforcement learning, machines can learn optimal strategies for path planning in autonomous driving.3. Advantages and challenges of deep reinforcement learning in autonomous driving1. AdvantagesDeep reinforcement learning has the following advantages in autonomous driving:(1) It can automatically complete tasks such as driving decision-making, behavior planning, and path planning, reducing manual participation and improving driving efficiency and safety.(2) The deep learning network can perform feature extraction and classification recognition on the images and videos collected by the sensor, so as to realize the state recognition of complex environments.(3) Deep reinforcement learning can learn the optimal strategy through the interaction with the environment, so as to realize the tasks of decision making, behavior planning and path planning in automatic driving.2. ChallengeDeep reinforcement learning also presents some challenges in autonomous driving:(1) Insufficient data: Deep reinforcement learning requires a large amount of data for training, but in the field of autonomous driving, it is very difficult to obtain large-scale driving data.(2) Safety: The safety of autonomous driving technology is an important issue, because once an accident occurs, its consequences will be unpredictable. Therefore, the use of deep reinforcement learning in autonomous driving requires very strict safety safeguards.(3) Interpretation performance: Deep reinforcement learning requires a lot of computing resources and time for training and optimization. Therefore, in practical applications, the problems of computing performance and time cost need to be considered.(4) Interpretability: Deep reinforcement learning models are usually black-box models, and their decision-making process is difficult to understand and explain, which will have a negative impact on the reliability and safety of autonomous driving systems. Therefore, how to improve the interpretability of deep reinforcement learning models is an important research direction.(5) Generalization ability: In the field of autonomous driving, vehicles are faced with various environments and situations. Therefore, the deep reinforcement learning model needs to have a strong generalization ability in order to be able to accurately and Safe decision-making and planning.In summary, deep reinforcement learning has great application potential in autonomous driving, but challenges such as data scarcity, safety, interpretability, computational performance, and generalization capabilities need to be addressed. Future research should address these issues and promote the development and application of deep reinforcement learning in the field of autonomous driving.深度强化学习在自动驾驶中的应用研究随着人工智能技术的不断发展和进步，自动驾驶技术已经成为了当前智能交通领域中的研究热点之一。
learningrate参数
learningrate参数什么是learning rate参数？如何选择合适的learning rate参数？在机器学习中，learning rate（学习率）是一种用于控制模型参数更新速度的超参数。
它决定了每次迭代时模型参数的变化程度。
选择合适的learning rate参数是训练模型的一个重要任务，因为一个合适的学习率能够加快模型的收敛速度，提高模型的准确性。
学习率的设置对机器学习算法的表现至关重要。
如果学习率设置得太小，模型在每次迭代中的参数变化将会较小，导致模型收敛缓慢；而如果学习率设置得太大，模型在每次迭代中的参数变化将会较大，模型的收敛性可能会受到影响，甚至无法收敛。
因此，选择一个合适的learning rate参数是控制模型训练过程中的一项重要任务。
选择合适的学习率的方法有多种，下面将逐步介绍常用的几种方法。
1. 固定学习率：最简单的选择是使用一个固定的学习率。
在训练过程中，此学习率不会改变，直到达到指定的停止条件。
这个方法通常使用在小数据集上，或者在尝试不同学习率的初步实验中。
2. 基于经验的学习率：有些情况下，经验可以帮助我们选择一个较好的学习率。
例如，如果先前的实验表明学习率为0.1可以取得良好的结果，那么可以尝试使用相同的学习率进行后续实验。
这种方法通常在相似问题上有用。
3. 网格搜索法：网格搜索是一种常用的调参方法，它通过穷举法尝试不同的学习率参数组合来找到最佳的学习率。
可以设置一个学习率范围，在该范围内均匀地选取多个学习率值，然后使用这些学习率参数进行模型的训练和评估。
最终，选择在验证集上表现最好的学习率进行模型的训练。
4. 自适应学习率：自适应学习率算法可以根据每次迭代的结果来动态地调整学习率。
常见的自适应学习率算法包括Adagrad、RMSprop和Adam 等。
Adagrad算法通过为每个参数分配一个不断累加的梯度平方和的衰减系数来减小学习率，从而使得学习率逐渐减小。
人工智能智能辅助学习 个性化学习评估
人工智能智能辅助学习个性化学习评估人工智能（Artificial Intelligence, AI）作为一项先进的技术，在教育领域的应用已经逐渐引起了广泛关注。
其中，辅助学习（Assisted Learning）以及个性化学习评估（Personalized Learning Assessment）是AI在教育领域中的重要应用之一。
本文将从这两个方面展开讨论，探讨人工智能智能辅助学习带来的教育变革以及个性化学习评估的优势和挑战。
一、人工智能智能辅助学习1.1 定义与概述人工智能智能辅助学习是指利用人工智能技术来提供学习者在教育过程中的辅助和支持。
通过智能化的系统和算法，学习者可以获得个性化的学习建议、知识点解释、交互等，并能根据学习者的情况进行调整和优化。
1.2 教育变革带来的好处人工智能智能辅助学习在教育中引起了革命性的变化。
首先，它可以根据学生的个体差异，为每个学生提供个性化的学习方案。
无论是在内容、难度还是学习节奏上，都能够更好地满足学生的需求，提高学习效果。
其次，AI技术可以通过数据的分析和挖掘，深入了解学生的学习过程和行为习惯。
通过对学生学习行为的分析，系统能够根据学生的学习偏好和弱点，提供个性化的学习支持和教学策略。
再次，人工智能智能辅助学习还可以提供及时的反馈和评估。
传统的学习过程中，学生通常需要等待作业或考试才能获得教师的评价和指导。
而智能辅助学习系统可以根据学生的答题情况和获得的知识点，实时给予评估和建议，帮助学生及时调整学习方向。
1.3 存在的挑战和解决办法尽管人工智能智能辅助学习带来了很多好处，但也面临一些挑战。
首先，隐私和安全问题是人工智能技术所面临的共同挑战，在学习过程中学生的个人信息有可能被滥用。
解决这个问题需要制定相关的隐私政策和法规，并加强对系统安全性的保障。
其次，系统的智能度和个性化程度也需要不断提高。
目前，很多人工智能辅助学习系统仍然是简单的题型匹配和知识点讲解，欠缺深度和广度。
机器学习知识：机器学习中的元学习
机器学习知识：机器学习中的元学习随着机器学习的发展，越来越多的研究者开始关注元学习(Meta Learning)。
元学习是指机器学习一个高级层面的学习任务，它是学习算法自动学习新的学习算法或更新参数，因而也被称为“学习如何学习”。
元学习的概念最早可以追溯到上世纪80年代之前，其中一个最早的工作是大卫·罗慕洛-哈特在1986年提出的“学习如何学习”（Learning to learn）的想法。
他将元学习定义为有关如何设计、验证、和分析一类学习算法的元层次问题。
这种方法使得学习算法可以自动地优化一个模型的学习过程，从而实现更好的性能。
元学习领域中有很多技术和方法，其中最有代表性的是元优化（Meta Optimization）和元学习算法自动设计（Automatic design of Metaalgorithm，简称ADAM）。
元优化是指通过学习如何优化学习器（如神经网络的优化器或梯度下降的超参数），来改善学习器的泛化能力。
传统的机器学习任务通常训练一个固定的模型来进行预测，而元优化能够改变学习过程中所使用的学习算法，从而可以改进模型的泛化能力。
在元学习算法自动设计中，通过优化器和模型的参数，自动设计出最优的学习算法。
这种方法是一种更加高效和自动化的方法，可以广泛应用于各种任务，使得机器学习算法的设计和优化更加全面和高效。
在目前的深度学习算法中，优化器也扮演着重要的角色，比如常见的梯度下降算法中的Adam、SGD等优化器，针对含有RNN、CapsNet 等结构的神经网络模型，在此基础上进一步改进和优化算法，可以达到更好的效果。
元学习通过对这些优化器的改进，使得学习器的泛化能力得到提高。
除了传统的机器学习任务之外，元学习还可以应用于强化学习领域中。
在强化学习中，元学习可以学习针对不同任务适用的最佳行为策略，从而使得强化学习的过程更加高效。
同时元学习还可以在训练过程中探索更多的方法和策略，自适应地快速获得更好的训练效果。
AI训练中的学习率衰减 提高模型性能的方法
AI训练中的学习率衰减提高模型性能的方法引言人工智能领域的发展正日益迅猛，而深度学习作为其中的重要分支，扮演着至关重要的角色。
在深度学习的模型训练过程中，学习率是一个关键的超参数，它决定了模型参数的更新速度。
正确设置学习率能够有效地提高模型的性能。
本文将介绍学习率衰减的概念以及如何使用学习率衰减方法来提高模型性能。
一、学习率衰减的概念学习率衰减是指随着训练步骤的进行，逐渐降低学习率的过程。
初始阶段，较大的学习率可以帮助模型快速收敛，但在后期可能会阻碍模型进一步的优化，甚至会使模型在最优点附近震荡。
因此，通过衰减学习率，可以保证模型在训练后期稳定收敛，从而提高模型性能。
二、学习率衰减的方法1. 固定衰减法固定衰减法是指在每一定步长后，将学习率按照固定的比例衰减。
这种方法简单直接，在一些简单模型上效果较好。
但是，由于固定的衰减比例可能不适用于所有的情况，因此在复杂的模型中可能表现不佳。
2. 按指数衰减法按指数衰减法是指按照指数函数来衰减学习率。
典型的衰减函数有指数衰减函数和平方根衰减函数。
指数衰减函数能够更快地降低学习率，适用于训练初期；而平方根衰减函数则对学习率进行了更平缓的衰减，适用于训练后期。
通过选择合适的衰减函数及其超参数，可以更加精确地控制学习率的衰减速度。
3. 定性调整法定性调整法是指根据模型的当前状态来调整学习率的策略。
常见的定性调整方法有损失曲线观察法、验证集表现观察法和梯度信息观察法等。
利用这些方法，可以根据实时的模型表现来动态地调整学习率，提高模型的训练效果。
4. 学习率退火法学习率退火法是指通过逐渐降低学习率来提高模型性能。
常见的学习率退火方法有余弦退火法和热重启法。
余弦退火法是一种“优雅降低”的策略，它通过余弦函数来衰减学习率，使得模型在训练后期更加稳定。
而热重启法则是在固定的间隔内周期性地重设学习率为初始值，从而帮助模型跳出局部最小值，进一步优化性能。
结论学习率衰减是深度学习模型训练中提高性能的重要手段之一。
- 1、下载文档前请自行甄别文档内容的完整性，平台不提供额外的编辑、内容补充、找答案等附加服务。
- 2、"仅部分预览"的文档,不可在线预览部分如存在完整性等问题,可反馈申请退款(可完整预览的文档不适用该条件!)。
- 3、如文档侵犯您的权益，请联系客服反馈,我们会尽快为您处理(人工客服工作时间：9:00-18:30)。
Submitted to the Journal of Autonomous Robots.Learning Traversability Models for AutonomousMobile VehiclesMichael Shneier, Tommy Chang, Tsai Hong, and Will ShacklefordNational Institute of Standards and TechnologyGaithersburg, MD 20899AbstractAutonomous mobile robots need to adapt their behavior to the terrain over which they drive, and to predict the traversability of the terrain so that they can effectively plan their paths. Such robots usually make use of a set of sensors to investigate the terrain around them and build up an internal representation that enable them to navigate. This paper ad-dresses the question of how to use sensor data to learn properties of the environment and use this knowledge to predict which regions of the environment are traversable. The ap-proach makes use of sensed information from range sensors (stereo or ladar), color cam-eras, and the vehicle’s navigation sensors. Models of terrain regions are learned from subsets of pixels that are selected by projection into a local occupancy grid. The models include color and texture and traversability information obtained from an analysis of the range data associated with the pixels. The models are learned entirely without supervi-sion, deriving their properties from the geometry and the appearance of the scene.The models are used to classify color images and assign traversability costs to regions. The classification does not use the range or position information, but only color images. Traversability determined during the model-building phase is stored in the models. This enables classification of regions beyond the range of stereo or ladar using the information in the color images. The paper describes how the models are constructed and maintained, how they are used to classify image regions, and how the system adapts to changing envi-ronments.Keywords: Learning, traversability, classification, color models, texture, range, mobile robotics1. IntroductionIf autonomous mobile robots are to become more generally useful, they must be able to adapt to new environments and learn from experience. To do so, they need a way to store pertinent information about the environment, recall the information at appropriate times, and reliably match stored information to newly-sensed data. They also must be able to modify the stored information to account for systematic changes in the environment. This paper describes an approach that addresses these problems for the situation where an autonomous vehicle must traverse unknown outdoor terrain and learn during travel how to distinguish areas that are traversable from those that are not.The approach is to make use of data from range sensors, color cameras, and position sen-sors to describe regions in the environment around the vehicle and to associate a cost of traversing each region with its description. Models of the terrain are learned using an un-supervised scheme that makes use of both geometric and appearance information.The vehicles we have developed run using a control hierarchy called 4D/RCS (Albus and Meystel, 2001;Albus et al., 2002). 4D/RCS provides a hierarchical organization of con-trol nodes, each of which divides the system into sensory perception (SP), world model-ing (WM) and behavior generation (BG) subsystems. Each 4D/RCS node is designed to carry out specific duties and responsibilities. Each node is assigned a specified span of control, both in terms of supervision of subordinates, and in terms of range and resolution in space and time. Interaction between SP, WM, and BG give rise to perception, cogni-tion, and reasoning. At lower levels in the hierarchy, representations of space and time are short-range and high-resolution. At nodes higher in the hierarchy, representations of space and time are long-range and low-resolution. This enables high-precision fast-action response from the low level control nodes, while higher level nodes are generating long-range plans and abstract concepts over broad regions of time and space. Typically, planning horizons expand by an order of magnitude in time and space at each higher level in the hierarchy. Within the WM of each node, a knowledge database provides a model of the external world at a range and resolution that is appropriate for the behavioral deci-sions that are the responsibility of that node.This paper is concerned mainly with the sensory processing and world modeling aspects of the hierarchy. It discusses the processing of multiple sensor inputs to generate models of terrain, and construction of traversability maps which are sent to the world model. There they provide input to path planners that generate trajectories to take the vehicle to its goal.We assume that the vehicle has at least the following sensors: a color camera, a range sensor that can measure range over an area (e.g., a stereo system), and an inertial naviga-tion system that provides an estimate of the vehicle’s position in space. The two vehicles for which the approach has been developed both have these sensors. NIST operates a High Mobility Multipurpose Wheeled Vehicle (HMMWV) that has several color cam-eras, including one mounted on top of an area-scanning ladar. As part of the Defense Ad-vanced Research Project Agency’s (DARPA) LAGR program (Learning Applied to Ground Robots), we also have a pair of small vehicles, each of which has twin color ste-reo systems. Both of these platforms provide range and color information to the vehicle. Each vehicle also has navigation sensors that provide position estimates. The vehicles have other sensors, which may be able to provide additional information to verify the classification results.The use of stereo vision has the advantage that the color and range data are already regis-tered. It has the disadvantages, however, of having a limited range, depending on the ste-reo baseline, and requiring sufficient texture in the scene to ensure that disparity can be measured. Ladar-based range sensors require registration with a color image and usually do not provide the same pixel resolution as a color camera, meaning that a window of color pixels may correspond to a single range measurement. On the other hand, ladar is fast, provides range to a greater distance, and is less affected by scene characteristics.Both types of sensor are suitable for our approach. The examples in this paper will be taken from the stereo cameras mounted on the LAGR platform, which is currently the most active research platform for this work.The availability of range information enables a robot to navigate largely using the geome-try of a scene. Another viable approach is to use topology of the surrounding space (DeSouza and Kak, 2002). Sensor processing is usually aimed at determining where the vehicle is and what parts of the world around it are traversable. The robot can then plan a path over the traversable region to get to its goal. Where range information is missing or unreliable, navigation is not so straightforward because it is less clear what constitutes clear ground. A typical range sensor will not be able to provide reliable range very far in front of the vehicle, and it is part of the aim of this work to extend the traversability analysis beyond the range sensing limit. This is done by associating traversability with appearance, under the assumption that regions that look similar will have similar traver-sability. Because there is no direct relationship between traversability and appearance, the system must learn the correspondence from experience.The appearance of regions in an image has been described in many ways, but most fre-quently in terms of color and/or texture. (Ulrich and Nourbakhsh, 2000b) used color im-agery to learn the appearance of a set of locations to enable a robot to recognize where it is. A set of images was recorded at each location and served as descriptors for that loca-tion. Images were represented by a set of one-dimensional histograms in both HLS (hue, luminance, saturation) and normalized Red, Green, and Blue (RGB) color spaces. When the robot needed to recognize its location, it compared its current image with the set of images associated with locations. To compare histograms when matching images, the Jef-frey divergence was used. The location was recognized as that associated with the best-matching stored image.In (Ulrich and Nourbakhsh, 2000a) the authors also addressed the issue of appearance-based obstacle detection using a single color camera and no range information. Their ap-proach makes the assumptions that the ground is flat and that the region directly in front of the robot is ground. This region is characterized by color histograms and used as a model for ground. In the domain of road detection, a related approach is described in (Tan et al., 2006). In principle, the method could be extended to deal with more classes, and our algorithm can be seen as one such extension that does not need to make the as-sumptions because of the availability of range information for regions close to the vehi-cle.Learning has been applied to computer vision for a variety of applications, including traversability prediction. (Wellington and Stentz, 2003) predicted the load-bearing sur-face under vegetation by extracting features from range data and associating them with the actual surface height measured when the vehicle drove over the corresponding terrain. The system learned a mapping from terrain features to surface height using a technique called locally weighted regression. Learning was done in a map domain. We also use a map in the current work, although it is a two dimensional (2D) rather than a three dimen-sional (3D) map, and we also make use of the information gained when driving over ter-rain to update traversability estimates, although not as the primary source of traversability information. The models we construct are not based on range information, however, since this would prevent the extrapolation of the traversability prediction to regions where range is not available.(Howard et al., 2001) presented a learning approach to determining terrain traversability based on fuzzy logic. A human expert was used to train a fuzzy terrain classifier based on terrain roughness and slope measures computed from stereo imagery. The fuzzy logic approach was also adopted by (Shirkhodaie et al., 2004), who applied a set of texture measures to windows of an image followed by a fuzzy classifier and region growing to locate traversable parts of the image.The problem faced by a robot of finding a path to a goal point is a feedback control prob-lem. The sensed feedback information comes from the cameras, Global Positioning Sys-tem (GPS), etc. The actuators are the drive motors on the wheels. The on-board com-puter implements the feedback controller that drives the vehicle position (part of the state) to the goal position. It is for this reason that there are similarities between learning methods for robots and the field of adaptive control (sometimes called learning control). The closest relationships are to the area of “on-line approximation based feedback con-trol” (Spooner et al., 2002), and in particular the “indirect adaptive control strategy” where a parameterized nonlinear map (e.g., implemented by a fuzzy or neural system) is adjusted to represent the process (environment) and then control decisions are based on that map. Stability, convergence, and robustness analysis is conducted for such feedback systems and principles of operation offer insights into the design of navigation methods for learning robots (e.g., the use of the notion of “probing” the environment vs. making progress toward reaching the goal, one of the most central ideas in adaptive control). Moreover, extended notions of adaptive control use learned models for planning and route selection by marrying ideas in adaptive and “model predictive control” (Passino, 2005). Indeed, the map-based strategy here is an excellent example of how successful such approaches can be.The notions of learning we use in this paper arose in the field of psychology. First, the most basic low levels of learning represented by the notions of “habituation” and “sensi-tization” (Domjan, 1998) are embedded in our algorithms. If the robot learns via multiple sensor inputs that an area is traversable, then it has been habituated to that input (it has learned to ignore information and go ahead and travel in a direction). Correspondingly, if the robot has learned that some sensory inputs correspond to a lack of traversability, then if such situations are encountered again the robot is sensitized and hence may not make the same attempts to travel through nontraversable areas as it did in the past. Such learn-ing in the form of habituation and sensitization sets the foundation for the elements of “classical and operant conditioning” (Domjan, 1998) that occur in our robot. Our cell up-date strategies correspond to learning strategies where via repeated sensory inputs it can learn to associate sensed features with a lack of traversability or good traversability so that the basics of classical conditioning are present. Indeed, our robot can exhibit the property of “blocking” since in learning it can initially use some sensed information to determine traversability, and then later when there are other learning opportunities, it willat times ignore new sensory information (model updates) since it is confident that for in-stance more sensory verification of the model is not needed. With respect to the “behav-iorist” approach to operant conditioning, if the robot senses some scene and it has learned that certain features are associated with rewards (getting closer to the goal by making forward progress), it will try to apply the same actions that were successful before, lead-ing to the “Thorndike’s effect” similar to what occurs in a “Skinner box” (Domjan, 1998). And, such opportunities for conditioning can occur during a single attempt by the robot to find a goal point via storage, updating, and later use of information in our maps as the robot travels. Moreover, our learned maps can be used between trials so that on successive attempts the robot learns how to direct its behavior to succeed even faster; hence, a basic property of “speed-up” in the rate of reward acquisition seen experimen-tally in rats in mazes (Domjan, 1998) can also be exhibited by our system. Finally, we note that our use of maps is quite similar to the idea that animals and humans build (learn) and use “cognitive maps” of their environment for planning spatial movement ((Halliday and Slater, 1983); (Schultz et al., 1997); (Gray, 1999)).The contributions of this paper include a fast learning algorithm that requires no training data to learn associations between appearance and traversability and a histogram-based representation of models that provides a well-defined way of comparing the models and matching them with sensed data. The models are described in terms of color and texture features that do not rely on range data. This enables them to be used to classify regions for which no range data are available. The models are learned from data selected to be close together in space, making it more likely that they are from the same physical re-gion. A further contribution is the introduction of a “ring” representation for recording the heading directions that are considered traversable by the learning system.These modules extend the 4D/RCS architecture by including learning of entities both in the maps kept by the World Model and as symbolically represented objects.The rest of the paper is organized as follows. First we introduce the problem to be ad-dressed. Next, we explain the algorithm and discuss how models are learned and how the classification is carried out. We then describe how the results are represented in both an occupancy grid and a data representation called a ring structure. We then present some examples to further explain how the system performs, and we end with a discussion. 2. Learning TraversabilityMany robotic vehicles can navigate successfully in open terrain or on highly constrained roads. Frequently, this capability is due to a careful provision of relevant information about the domain in which the vehicle will operate. The problem we address in this paper is to determine how to introduce a learning capability to the robot that will enable it to decide for itself the traversability of the terrain around it, based on input from its sensors and its experience of traveling over similar terrain in the past.The DARPA LAGR program (Jackel, 2005) aims to extend the navigation capabilities of robotic vehicles by enabling them to learn from experience, from examples, and from be-ing taught. Through monthly tests, new challenges are introduced to the LAGR partici-pants, whose software must evolve to operate in more and more complex environments. The LAGR project provided the robotic platform to the participants (Figure 1) and by the nature of the tests ensures that the vehicles and their low-level control systems would re-main unaltered. This ensures that all the development focuses on perception and control strategies that learn to improve their performance. The primary sensors on the LAGR ve-hicles include two pairs of stereo cameras, physical and infra-red bumpers in front of the vehicle, and a position-detection system consisting of a Global Positioning System (GPS)Figure 1. The LAGR vehiclesensor and an inertial navigation system.The tests in the LAGR program take the form of navigating the vehicle from a defined start point to a fixed goal point. This requires avoiding obstacles such as trees, fences, or various objects introduced into the environment by the LAGR administrators conducting the tests. The vehicle uses its sensors to build a model of the world around it and plans a path from the start to the goal. In many cases, obstacles are placed along the path in such a way as to ensure that a straight-line path to the goal is not traversable. Also, the course may be set up in such a way that by the time the stereo sensors or bumpers detect an ob-stacle, the vehicle has entered a region that requires a long detour to reach the goal. Teams are given three chances to reach the goal. The idea is that early runs will enable the robot to learn which regions to avoid and which to seek out, so that by the third run it has determined the most efficient path. The vehicle has no a priori knowledge of the kind of terrain it will traverse, so it must learn as it goes along, by observing both the geome-try and the appearance of the terrain.Learning may include remembering the path the vehicle took in previous runs or the re-gions seen by the sensors during those runs. In our approach, both of these types of learn-ing are included but, as described in this paper, we also try to learn a relationship between the appearance of the terrain and its observed traversability. An advantage of this kind of learning is that regions that are too far away for reliable stereo (and hence reliable obsta-cle detection) can be identified as either desirable or undesirable for the vehicle to trav-erse. This enables the vehicle to plan further ahead and avoid entering traps that prevent it from reaching the goal. Remembering the learned models also allows the vehicle to navi-gate when stereo is not available3. The AlgorithmThe autonomous vehicle relies on its sensors to describe the terrain over which it is trav-eling. Sensor processing must interpret the raw data and extract from it information use-ful for planning. This includes topological information, such as slopes and ditches, and feature-based information, such as obstacles and ground cover. While some of the topo-logical information can be extracted from the range data fairly easily, other features are harder to identify and their properties are not usually obvious from analysis of the sen-sory data. For example, the traversability of tall grass cannot be determined from range and color information alone, so additional information must be provided through some other means. Often, this is part of the a priori information built in to the system, meaning that the vehicle only has to recognize regions as tall grass to be able to associate a traver-sability value with them. In this paper, we develop a method that enables the vehicle to learn the traversability of different regions from experience.We develop an algorithm that first analyzes the range data to locate regions correspond-ing to ground and to obstacles. Next, this information is used, along with the range and color data, to construct models of the appearance of regions. These models include an estimate of the cost of traversing the regions. Finally, the models are used to segment and classify regions in the color images. Associating regions with models enables traversabil-ity costs to be assigned to areas where there is no range data and thus no directly measur-able obstacles. As the vehicle traverses the terrain, more direct information is gathered to refine the traversability costs. This includes noting which regions are actually traversed and adjusting the traversability of the associated models. It also involves adjusting the traversability of regions where the vehicle’s mechanical bumper is triggered, and where the wheels slip or the engine has to strain to move the vehicle.3.1. Building the ModelsFirst, we construct a local occupancy map (Figure 2) consisting of a grid of cells, each of which represents a fixed square region in the world projected onto a nominal ground plane. Currently we use an array of 201x201 cells, each of size 0.2 m square, giving a map of size 40 m on a side. The map is always oriented with one axis pointing north and the other east. The map scrolls under the vehicle as the vehicle moves, and cells that scroll off the end of the map are forgotten. Cells that move onto the map are cleared and made ready for new information. Note that if the vehicle moves far enough, the entire map will change. If it then returns to a place it has previously traversed, the information known about that location will be lost. In principle, it would be straightforward to re-member all information learned as the vehicle moves about. Alternatively, we could usemaps of different resolutions and a strategy for storing abstracted information for later use into a global map as the occupancy grid moves out of a region. Simultaneously, the cells that the occupancy grid moves into can be filled in using the pre-stored abstract in-formation and this can be better than using no information at all. Such a strategy was found to be highly effective in learning control in a feedback system (Kwong and Pass-ino, 1996). However, for our application the storage of all information or even abstracted versions of it is not generally useful because of errors in the navigation system, which grow as the vehicle moves. This means that when it comes time to restore the contents of a cell it may be hard to decide which stored cell should be used.The next step is to process the range data and locate obstacles and ground regions. Obsta-cles are defined as objects that project more than some distance d above or below the ground. Positive obstacles are detected in the range images, while negative obstacles are detected in the world model map (Chang et al., 1999). The algorithm scans column by column in the image, starting with a point known to be on the ground. An initial ground value is assigned at the location where the front wheels of the vehicle touch the ground, known from Inertial Navigational System (INS) and GPS sensors. A pixel is labeled a positive obstacle if it rises high enough and abruptly enough from the ground plane. The negative obstacle detection algorithm maintains its own high-resolution ground map cen-tered on the vehicle. This ground map contains all the projected ground pixels detected by the positive obstacle detection module. The algorithm first identifies the pixels in the range image that potentially correspond to a negative obstacle because they are below the ground level and are large enough. For efficiency, the algorithm detects only the borders of negative obstacles. The algorithm is described in detail in the Appendix.The model-building algorithm takes as input the color image, the associated and regis-tered range data (x, y, z points), and the labels (GROUND and OBSTACLE) computed by the obstacle-detection step. It builds models by segmenting the color image into re-gions with uniform properties. Note that only points that have associated range values are used. The process works as follows:When a data set becomes available for processing, the map is scrolled so that the vehicle occupies the center cell of the map. Each point of the data set consists of a vector contain-ing three color values, red (R), green (G), and blue (B). The vector also contains the 3D position of the point (x, y, z), and a label from the obstacle detection step. Currently we consider only OBSTACLE and GROUND labels, although the obstacle detection algo-rithm identifies other regions, such as overhanging objects. Each point is processed as follows.Figure 2. An occupancy grid with the vehicle in the center.1.If the point is not labeled as GROUND or OBSTACLE, it is skipped (Other labelscan be treated without significant changes to the algorithm). Points that do not have associated range values are also skipped.2.Points that pass step 1 are projected into the map. This is possible because the x,y, and z values of the point are known as is the pose of the vehicle. If a point pro-jects outside the map it is skipped. Each cell receives all points that fall within the square region in the world determined by the location of the cell, regardless of the height of the point above the ground. The cell to which the point pro-jects accumulates information that summarizes the characteristics of all points seen by this cell. This includes color, texture, and contrast properties of the pro-jected points, as well as the number of OBSTACLE and GROUND points that have projected into the cell.Color is represented by ratios R/G, R/B, and G/B rather than directly using R, G, and B. This provides a small amount of protection from the color of ambient illu-mination. Each color ratio is represented by an 8-bin histogram, representing val-ues from 0 to 255. The values are actually stored in a normalized form, meaning that the values can be viewed as probabilities of the occurrence of each ratio. Tex-ture and contrast are computed using Local Binary Patterns (LBP) (Ojala et al., 1996). These patterns represent the relationships between pixels in a 3x3neighborhood in the image, and their values range from 0 to 255. Similarly to the color ratios, the texture measure is represented by a histogram with 8 bins, also normalized. Contrast is represented by a single number ranging from 0 to 1.Local Binary Patterns are computed on 3×3 windows, as follows (Figure 3). First, the center pixel value is used to threshold the other pixels in the window (Figure 3b). Then a weighted sum is computed of the eight surrounding thresholded points (Figure 3d). The weights are assigned as powers of 2 (Figure 3c), so that each location has a unique weight (index). Given that there are eight surround pix-els, and each has value 0 or 1 after thresholding, the final value assigned by the operator to the central pixel can be represented by an eight-bit byte, making the implementation very efficient. The LBP values are combined with a contrast measure at each point, computed over the same window.(a)(b)(c) (d) Figure 3. (a) A 3x 3 neighborhood. (b) Result of thresholding by middle value. (c) Weights applied to each thresholded pixel. (d) Resulting value in the center cell is sum of the weighted thresholded values.3. When a cell accumulates enough points it is ready to be considered as a model. While it would be best to have a statistically meaningful way of deciding when enough points have been seen, we currently use a threshold determined by ex-periment. In order to build a model, we require that a minimum percentage (cur-rently 95%) of the points projected into a cell have the same label (OBSTACLE or GROUND). Given how small a region in space the cells represent, this is mostly the case. If a cell is the first to accumulate enough points, its values are simply copied to instantiate the first model. Models have exactly the same struc-ture as cells, so this is trivial.If there are already defined models, the cell must first be matched to the existing models to see if it can be merged or if a new model must be created. Matching is done by computing a score, Dist , as a weighted sum of the elements of the model, m , and the cell c . That is,),(),(m c f w m c Dist i i ∑=where i f is either a measure of the similarity of two histograms or, in the case of contrast, is the absolute value of the difference of the two contrast values,c m contrast contrast contrast f −=. The histograms are always stored normalized by the number of points. Various measures h f of the similarity of two histograms (discrete probability density functions) can be used, such as a Chi Squared test or。