Cascade-correlation级联相关-27页文档资料
人工智能专业词汇
Letter AAccumulated error backpropagation 累积误差逆传播Activation Function 激活函数Adaptive Resonance Theory/ART 自适应谐振理论Addictive model 加性学习Adversarial Networks 对抗网络Affine Layer 仿射层Affinity matrix 亲和矩阵Agent 代理/ 智能体Algorithm 算法Alpha-beta pruning α-β剪枝Anomaly detection 异常检测Approximation 近似Area Under ROC Curve／AUC Roc 曲线下面积Artificial General Intelligence/AGI 通用人工智能Artificial Intelligence/AI 人工智能Association analysis 关联分析Attention mechanism 注意力机制Attribute conditional independence assumption 属性条件独立性假设Attribute space 属性空间Attribute value 属性值Autoencoder 自编码器Automatic speech recognition 自动语音识别Automatic summarization 自动摘要Average gradient 平均梯度Average—Pooling 平均池化Letter BBackpropagation Through Time 通过时间的反向传播Backpropagation/BP 反向传播Base learner 基学习器Base learning algorithm 基学习算法Batch Normalization/BN 批量归一化Bayes decision rule 贝叶斯判定准则Bayes Model Averaging／BMA 贝叶斯模型平均Bayes optimal classifier 贝叶斯最优分类器Bayesian decision theory 贝叶斯决策论Bayesian network 贝叶斯网络Between—class scatter matrix 类间散度矩阵Bias 偏置/ 偏差Bias-variance decomposition 偏差—方差分解Bias—Variance Dilemma 偏差–方差困境Bi-directional Long—Short Term Memory/Bi—LSTM 双向长短期记忆Binary classification 二分类Binomial test 二项检验Bi-partition 二分法Boltzmann machine 玻尔兹曼机Bootstrap sampling 自助采样法／可重复采样／有放回采样Bootstrapping 自助法Break—Event Point／BEP 平衡点Letter CCalibration 校准Cascade—Correlation 级联相关Categorical attribute 离散属性Class-conditional probability 类条件概率Classification and regression tree/CART 分类与回归树Classifier 分类器Class—imbalance 类别不平衡Closed —form 闭式Cluster 簇/类/集群Cluster analysis 聚类分析Clustering 聚类Clustering ensemble 聚类集成Co-adapting 共适应Coding matrix 编码矩阵COLT 国际学习理论会议Committee—based learning 基于委员会的学习Competitive learning 竞争型学习Component learner 组件学习器Comprehensibility 可解释性Computation Cost 计算成本Computational Linguistics 计算语言学Computer vision 计算机视觉Concept drift 概念漂移Concept Learning System /CLS 概念学习系统Conditional entropy 条件熵Conditional mutual information 条件互信息Conditional Probability Table／CPT 条件概率表Conditional random field/CRF 条件随机场Conditional risk 条件风险Confidence 置信度Confusion matrix 混淆矩阵Connection weight 连接权Connectionism 连结主义Consistency 一致性／相合性Contingency table 列联表Continuous attribute 连续属性Convergence 收敛Conversational agent 会话智能体Convex quadratic programming 凸二次规划Convexity 凸性Convolutional neural network/CNN 卷积神经网络Co—occurrence 同现Correlation coefficient 相关系数Cosine similarity 余弦相似度Cost curve 成本曲线Cost Function 成本函数Cost matrix 成本矩阵Cost—sensitive 成本敏感Cross entropy 交叉熵Cross validation 交叉验证Crowdsourcing 众包Curse of dimensionality 维数灾难Cut point 截断点Cutting plane algorithm 割平面法Letter DData mining 数据挖掘Data set 数据集Decision Boundary 决策边界Decision stump 决策树桩Decision tree 决策树／判定树Deduction 演绎Deep Belief Network 深度信念网络Deep Convolutional Generative Adversarial Network/DCGAN 深度卷积生成对抗网络Deep learning 深度学习Deep neural network/DNN 深度神经网络Deep Q-Learning 深度Q 学习Deep Q—Network 深度Q 网络Density estimation 密度估计Density-based clustering 密度聚类Differentiable neural computer 可微分神经计算机Dimensionality reduction algorithm 降维算法Directed edge 有向边Disagreement measure 不合度量Discriminative model 判别模型Discriminator 判别器Distance measure 距离度量Distance metric learning 距离度量学习Distribution 分布Divergence 散度Diversity measure 多样性度量／差异性度量Domain adaption 领域自适应Downsampling 下采样D—separation (Directed separation）有向分离Dual problem 对偶问题Dummy node 哑结点Dynamic Fusion 动态融合Dynamic programming 动态规划Letter EEigenvalue decomposition 特征值分解Embedding 嵌入Emotional analysis 情绪分析Empirical conditional entropy 经验条件熵Empirical entropy 经验熵Empirical error 经验误差Empirical risk 经验风险End-to—End 端到端Energy-based model 基于能量的模型Ensemble learning 集成学习Ensemble pruning 集成修剪Error Correcting Output Codes／ECOC 纠错输出码Error rate 错误率Error—ambiguity decomposition 误差—分歧分解Euclidean distance 欧氏距离Evolutionary computation 演化计算Expectation-Maximization 期望最大化Expected loss 期望损失Exploding Gradient Problem 梯度爆炸问题Exponential loss function 指数损失函数Extreme Learning Machine/ELM 超限学习机Letter FFactorization 因子分解False negative 假负类False positive 假正类False Positive Rate/FPR 假正例率Feature engineering 特征工程Feature selection 特征选择Feature vector 特征向量Featured Learning 特征学习Feedforward Neural Networks/FNN 前馈神经网络Fine—tuning 微调Flipping output 翻转法Fluctuation 震荡Forward stagewise algorithm 前向分步算法Frequentist 频率主义学派Full-rank matrix 满秩矩阵Functional neuron 功能神经元Letter GGain ratio 增益率Game theory 博弈论Gaussian kernel function 高斯核函数Gaussian Mixture Model 高斯混合模型General Problem Solving 通用问题求解Generalization 泛化Generalization error 泛化误差Generalization error bound 泛化误差上界Generalized Lagrange function 广义拉格朗日函数Generalized linear model 广义线性模型Generalized Rayleigh quotient 广义瑞利商Generative Adversarial Networks/GAN 生成对抗网络Generative Model 生成模型Generator 生成器Genetic Algorithm/GA 遗传算法Gibbs sampling 吉布斯采样Gini index 基尼指数Global minimum 全局最小Global Optimization 全局优化Gradient boosting 梯度提升Gradient Descent 梯度下降Graph theory 图论Ground-truth 真相／真实Letter HHard margin 硬间隔Hard voting 硬投票Harmonic mean 调和平均Hesse matrix 海塞矩阵Hidden dynamic model 隐动态模型Hidden layer 隐藏层Hidden Markov Model/HMM 隐马尔可夫模型Hierarchical clustering 层次聚类Hilbert space 希尔伯特空间Hinge loss function 合页损失函数Hold—out 留出法Homogeneous 同质Hybrid computing 混合计算Hyperparameter 超参数Hypothesis 假设Hypothesis test 假设验证Letter IICML 国际机器学习会议Improved iterative scaling/IIS 改进的迭代尺度法Incremental learning 增量学习Independent and identically distributed/i。
Canonical Correlation Analysis
典型相关分析(moon)Canonical Correlation Analysis简单相关系数（即普通回归方法）描述两组变量的相关关系的缺点:只是孤立考虑单个X与单个Y间的相关，没有考虑X、Y变量组内部各变量间的相关。
两组间有许多简单相关系数，使问题显得复杂，难以从整体描述。
典型相关是简单相关、多重相关的推广。
典型相关是研究两组变量之间相关性的一种统计分析方法。
也是一种降维技术。
1936年，Hotelling提出典型相关分析。
考虑两组变量的线性组合, 并研究它们之间的相关系数p(u,v).在所有的线性组合中, 找一对相关系数最大的线性组合, 用这个组合的单相关系数来表示两组变量的相关性, 叫做两组变量的典型相关系数, 而这两个线性组合叫做一对典型变量。
在两组多变量的情形下, 需要用若干对典型变量才能完全反映出它们之间的相关性。
下一步, 再在两组变量的与u1,v1不相关的线性组合中, 找一对相关系数最大的线性组合,它就是第二对典型变量, 而且p(u2,v2)就是第二个典型相关系数。
这样下去, 可以得到若干对典型变量, 从而提取出两组变量间的全部信息。
为了研究两组变量的关系，如果在理论上能解释谁是自变量，谁是因变量，自然就做路径分析（最好用Lisrel或者Amos 等软件，用SPSS应该不够科学）。
如果不能辨别两组变量谁是是自变量，谁是因变量，那再用回归就不恰当的，有一种多对多的相关可以使用，那就是典型相关Canonical correlation在作两组变量和的典型相关分析之前，首先应检验两组变量是否相关。
如果不相关，则讨论两组变量的典型相关就毫无意义。
典型相关分析的实质就是在两组随机变量中选取若干个有代表性的综合指标（变量的线性组合）, 用这些指标的相关关系来表示原来的两组变量的相关关系。
这在两组变量的相关性分析中, 可以起到合理的简化变量的作用; 当典型相关系数足够大时, 可以像回归分析那样, 由- 组变量的数值预测另一组变量的线性组合的数值。
数字信号处理英语词汇
AAbsolutely integrable 绝对可积Absolutely integrable impulse response 绝对可积冲激响应Absolutely summable 绝对可和Absolutely summable impulse response 绝对可和冲激响应Accumulator 累加器Acoustic 声学Adder 加法器Additivity property 可加性Aliasing 混叠现象All-pass systems 全通系统AM (Amplitude modulation ) 幅度调制Amplifier 放大器Amplitude modulation (AM) 幅度调制Amplitude-scaling factor 幅度放大因子Analog-to-digital (A-to-D) converter 模数转换器Analysis equation 分析公式（方程）Angel (phase) of complex number 复数的角度（相位）Angle criterion 角判据Angle modulation 角度调制Anticausality 反因果Aperiodic 非周期Aperiodic convolution 非周期卷积Aperiodic signal 非周期信号Asynchronous 异步的Audio systems 音频（声音）系统Autocorrelation functions 自相关函数Automobile suspension system 汽车减震系统Averaging system 平滑系统BBand-limited 带（宽）限的Band-limited input signals 带限输入信号Band-limited interpolation 带限内插Bandpass filters 带通滤波器Bandpass signal 带通信号Bandpass-sampling techniques 带通采样技术Bandwidth 带宽Bartlett (triangular) window 巴特利特（三角形）窗Bilateral Laplace transform 双边拉普拉斯变换Bilinear 双线性的Bilinear transformation 双线性变换Bit （二进制）位，比特Block diagrams 方框图Bode plots 波特图Bounded 有界限的Break frequency 折转频率Butterworth filters 巴特沃斯滤波器C“Chirp” transform algorithm“鸟声”变换算法Capacitor 电容器Carrier 载波Carrier frequency 载波频率Carrier signal 载波信号Cartesian (rectangular) form 直角坐标形式Cascade (series) interconnection 串联，级联Cascade-form 串联形式Causal LTI system 因果的线性时不变系统Channel 信道，频道Channel equalization 信道均衡Chopper amplifier 斩波器放大器Closed-loop 闭环Closed-loop poles 闭环极点Closed-loop system 闭环系统Closed-loop system function 闭环系统函数Coefficient multiplier 系数乘法器Coefficients 系数Communications systems 通信系统Commutative property 交换性（交换律）Compensation for nonideal elements 非理想元件的补偿Complex conjugate 复数共轭Complex exponential carrier 复指数载波Complex exponential signals 复指数信号Complex exponential(s) 复指数Complex numbers 复数Conditionally stable systems 条件稳定系统Conjugate symmetry 共轭对称Conjugation property 共轭性质Continuous-time delay 连续时间延迟Continuous-time filter 连续时间滤波器Continuous-time Fourier series 连续时间傅立叶级数Continuous-time Fourier transform 连续时间傅立叶变换Continuous-time signals 连续时间信号Continuous-time systems 连续时间系统Continuous-to-discrete-time conversion 连续时间到离散时间转换Convergence 收敛Convolution 卷积Convolution integral 卷积积分Convolution property 卷积性质Convolution sum 卷积和Correlation function 相关函数Critically damped systems 临界阻尼系统Crosss-correlation functions 互相关函数Cutoff frequencies 截至频率DDamped sinusoids 阻尼正弦振荡Damping ratio 阻尼系数Dc offset 直流偏移Dc sequence 直流序列Deadbeat feedback systems 临界阻尼反馈系统Decibels (dB) 分贝Decimation 抽取Decimation and interpolation 抽取和内插Degenerative (negative) feedback 负反馈Delay 延迟Delay time 延迟时间Demodulation 解调Difference equations 差分方程Differencing property 差分性质Differential equations 微分方程Differentiating filters 微分滤波器Differentiation property 微分性质Differentiator 微分器Digital-to-analog (D-to-A) converter 数模转换器Direct Form I realization 直接I型实现Direct form II realization 直接II型实现Direct-form 直接型Dirichlet conditions 狄里赫利条件Dirichlet, P.L. 狄里赫利Discontinuities 间断点，不连续Discrete-time filters 离散时间滤波器Discrete-time Fourier series 离散时间傅立叶级数Discrete-time Fourier series pair 离散时间傅立叶级数对Discrete-time Fourier transform （DFT）离散时间傅立叶变换Discrete-time LTI filters 离散时间线性时不变滤波器Discrete-time modulation 离散时间调制Discrete-time nonrecursive filters 离散时间非递归滤波器Discrete-time signals 离散时间信号Discrete-time systems 离散时间系统Discrete-time to continuous-time conversion 离散时间到连续时间转换Dispersion 弥撒（现象）Distortion 扭曲，失真Distribution theory（property）分配律Dominant time constant 主时间常数Double-sideband modulation (DSB) 双边带调制Downsampling 减采样Duality 对偶性EEcho 回波Eigenfunctions 特征函数Eigenvalue 特征值Elliptic filters 椭圆滤波器Encirclement property 围线性质End points 终点Energy of signals 信号的能量Energy-density spectrum 能量密度谱Envelope detector 包络检波器Envelope function 包络函数Equalization 均衡化Equalizer circuits 均衡器电路Equation for closed-loop poles 闭环极点方程Euler, L. 欧拉Euler’s relation欧拉关系（公式）Even signals 偶信号Exponential signals 指数信号Exponentials 指数FFast Fourier transform (FFT) 快速傅立叶变换Feedback 反馈Feedback interconnection 反馈联结Feedback path 反馈路径Filter(s) 滤波器Final-value theorem 终值定理Finite impulse response (FIR) 有限长脉冲响应Finite impulse response (FIR) filters 有限长脉冲响应滤波器Finite sum formula 有限项和公式Finite-duration signals 有限长信号First difference 一阶差分First harmonic components 基波分量（一次谐波分量）First-order continuous-time systems 一阶连续时间系统First-order discrete-time systems 一阶离散时间系统First-order recursive discrete-time filters 一阶递归离散时间滤波器First-order systems 一阶系统Forced response 受迫响应Forward path 正向通路Fourier series 傅立叶级数Fourier transform 傅立叶变换Fourier transform pairs 傅立叶变换对Fourier, Jean Baptiste Joseph 傅立叶（法国数学家，物理学家）Frequency response 频率响应Frequency response of LTI systems 线性时不变系统的频率响应Frequency scaling of continuous-time Fourier transform 连续时间傅立叶变化的频率尺度（变换性质）Frequency shift keying (FSK) 频移键控Frequency shifting property 频移性质Frequency-division multiplexing (FDM) 频分多路复用Frequency-domain characterization 频域特征Frequency-selective filter 频率选择滤波器Frequency-shaping filters 频率成型滤波器Fundamental components 基波分量Fundamental frequency 基波频率Fundamental period 基波周期GGain 增益Gain and phase margin 增益和相位裕度General complex exponentials 一般复指数信号Generalized functions 广义函数Gibbs phenomenon 吉伯斯现象Group delay 群延迟HHalf-sample delay 半采样间隔时延Hanning window 汉宁窗Harmonic analyzer 谐波分析议Harmonic components 谐波分量Harmonically related 谐波关系Heat propagation and diffusion 热传播和扩散现象Higher order holds 高阶保持Highpass filter 高通滤波器Highpass-to-lowpass transformations 高通到低通变换Hilbert transform 希尔波特滤波器Homogeneity (scaling) property 齐次性（比例性）IIdeal 理想的Ideal bandstop characteristic 理想带阻特征Ideal frequency-selective filter 理想频率选择滤波器Idealization 理想化Identity system 恒等系统Imaginary part 虚部Impulse response 冲激响应Impulse train 冲激串Incrementally linear systems 增量线性系统Independent variable 独立变量Infinite impulse response (IIR) 无限长脉冲响应Infinite impulse response (IIR) filters 无限长脉冲响应滤波器Infinite sum formula 无限项和公式Infinite taylor series 无限项泰勒级数Initial-value theorem 初值定理Inpulse-train sampling 冲激串采样Instantaneous 瞬时的Instantaneous frequency 瞬时频率Integration in time-domain 时域积分Integration property 积分性质Integrator 积分器Interconnection 互联Intermediate-frequency (IF) stage 中频级Intersymbol interference (ISI) 码间干扰Inverse Fourier transform 傅立叶反变换Inverse Laplace transform 拉普拉斯反变换Inverse LTI system 逆线性时不变系统Inverse system design 逆系统设计Inverse z-transform z反变换Inverted pendulum 倒立摆Invertibility of LTI systems 线性时不变系统的可逆性Invertible systems 逆系统LLag network 滞后网络Lagrange, J.L. 拉格朗日（法国数学家，力学家）Laplace transform 拉普拉斯变换Laplace, P.S. de 拉普拉斯（法国天文学家，数学家）lead network 超前网络left-half plane 左半平面left-sided signal 左边信号Linear 线性Linear constant-coefficient difference equations 线性常系数差分方程Linear constant-coefficient differential equations 线性常系数微分方程Linear feedback systems 线性反馈系统Linear interpolation 线性插值Linearity 线性性Log magnitude-phase diagram 对数幅－相图Log-magnitude plots 对数模图Lossless coding 无损失码Lowpass filters 低通滤波器Lowpass-to-highpass transformation 低通到高通的转换LTI system response 线性时不变系统响应LTI systems analysis 线性时不变系统分析MMagnitude and phase 幅度和相位Matched filter 匹配滤波器Measuring devices 测量仪器Memory 记忆Memoryless systems 无记忆系统Modulating signal 调制信号Modulation 调制Modulation index 调制指数Modulation property 调制性质Moving-average filters 移动平均滤波器Multiplexing 多路技术Multiplication property 相乘性质Multiplicities 多样性NNarrowband 窄带Narrowband frequency modulation 窄带频率调制Natural frequency 自然响应频率Natural response 自然响应Negative (degenerative) feedback 负反馈Nonanticipatibe system 不超前系统Noncausal averaging system 非因果平滑系统Nonideal 非理想的Nonideal filters 非理想滤波器Nonmalized functions 归一化函数Nonrecursive 非递归Nonrecursive filters 非递归滤波器Nonrecursive linear constant-coefficient difference非递归线性常系数差分方程equationsNyquist frequency 奈奎斯特频率Nyquist rate 奈奎斯特率Nyquist stability criterion 奈奎斯特稳定性判据OOdd harmonic 奇次谐波Odd signal 奇信号Open-loop 开环Open-loop frequency response 开环频率响应Open-loop system 开环系统Operational amplifier 运算放大器Orthogonal functions 正交函数Orthogonal signals 正交信号Oscilloscope 示波器Overdamped system 过阻尼系统Oversampling 过采样Overshoot 超量PParallel interconnection 并联Parallel-form block diagrams 并联型框图Parity check 奇偶校验检查Parseval’s relatio n 帕斯伐尔关系（定理）Partial-fraction expansion 部分分式展开Particular and homogeneous solution 特解和齐次解Passband 通频带Passband edge 通带边缘Passband frequency 通带频率Passband ripple 通带起伏（或波纹）Pendulum 钟摆Percent modulation 调制百分数Periodic 周期的Periodic complex exponentials 周期复指数Periodic convolution 周期卷积Periodic signals 周期信号Periodic square wave 周期方波Periodic square-wave modulating signal 周期方波调制信号Periodic train of impulses 周期冲激串Phase (angle) of complex number 复数相位（角度）Phase lag 相位滞后Phase lead 相位超前Phase margin 相位裕度Phase shift 相移Phase-reversal 相位倒置Phase modulation 相位调制Plant 工厂Polar form 极坐标形式Poles 极点Pole-zero plot(s) 零极点图Polynomials 多项式Positive (regenerative) feedback 正（再生）反馈Power of signals 信号功率Power-series expansion method 幂级数展开的方法Principal-phase function 主值相位函数Proportional (P) control 比例控制Proportional feedback system 比例反馈系统Proportional-plus-derivative 比例加积分Proportional-plus-derivative feedback 比例加积分反馈Proportional-plus-integral-plus-differential (PID) control 比例－积分－微分控制Pulse-amplitude modulation 脉冲幅度调制Pulse-code modulation 脉冲编码调制Pulse-train carrier 冲激串载波QQuadrature distortion 正交失真Quadrature multiplexing 正交多路复用Quality of circuit 电路品质（因数）RRaised consine frequency response 升余弦频率响应Rational frequency responses 有理型频率响应Rational transform 有理变换RC highpass filter RC 高阶滤波器RC lowpass filter RC 低阶滤波器Real 实数Real exponential signals 实指数信号Real part 实部Rectangular (Cartesian) form 直角（卡笛儿）坐标形式Rectangular pulse 矩形脉冲Rectangular pulse signal 矩形脉冲信号Rectangular window 矩形窗口Recursive (infinite impulse response) filters 递归（无时限脉冲响应）滤波器Recursive linear constant-coefficient difference equations 递归的线性常系数差分方程Regenerative (positive) feedback 再生（正）反馈Region of comvergence 收敛域right-sided signal 右边信号Rise time 上升时间Root-locus analysis 根轨迹分析（方法）Running sum 动求和SS domain S域Sampled-data feedback systems 采样数据反馈系统Sampled-data systems 采样数据系统Sampling 采样Sampling frequency 采样频率Sampling function 采样函数Sampling oscilloscope 采样示波器Sampling period 采样周期Sampling theorem 采样定理Scaling (homogeneity) property 比例性（齐次性）性质Scaling in z domain z域尺度变换Scrambler 扰频器Second harmonic components 二次谐波分量Second-order 二阶Second-order continuous-time system 二阶连续时间系统Second-order discrete-time system 二阶离散时间系统Second-order systems 二阶系统sequence 序列Series (cascade) interconnection 级联（串联）Sifting property 筛选性质Sinc functions sinc函数Single-sideband 单边带Single-sideband sinusoidal amplitude modulation 单边带正弦幅度调制Singularity functions 奇异函数Sinusoidal 正弦（信号）Sinusoidal amplitude modulation 正弦幅度调制Sinusoidal carrier 正弦载波Sinusoidal frequency modulation 正弦频率调制Sliding 滑动Spectral coefficient 频谱系数Spectrum 频谱Speech scrambler 语音加密器S-plane S平面Square wave 方波Stability 稳定性Stabilization of unstable systems 不稳定系统的稳定性（度）Step response 阶跃响应Step-invariant transformation 阶跃响应不定的变换Stopband 阻带Stopband edge 阻带边缘Stopband frequency 阻带频率Stopband ripple 阻带起伏（或波纹）Stroboscopic effect 频闪响应Summer 加法器Superposition integral 叠加积分Superposition property 叠加性质Superposition sum 叠加和Suspension system 减震系统Symmetric periodic 周期对称Symmetry 对称性Synchronous 同步的Synthesis equation 综合方程System function(s) 系统方程TTable of properties 性质列表Taylor series 泰勒级数Time 时间，时域Time advance property of unilateral z-transform 单边z变换的时间超前性质Time constants 时间常数Time delay property of unilateral z-transform 单边z变换的时间延迟性质Time expansion property 时间扩展性质Time invariance 时间变量Time reversal property 时间反转（反褶）性Time scaling property 时间尺度变换性Time shifting property 时移性质Time window 时间窗口Time-division multiplexing (TDM) 时分复用Time-domain 时域Time-domain properties 时域性质Tracking system (s) 跟踪系统Transfer function 转移函数transform pairs 变换对Transformation 变换（变形）Transition band 过渡带Transmodulation (transmultiplexing) 交叉调制Triangular (Barlett) window 三角型（巴特利特）窗口Trigonometric series 三角级数Two-sided signal 双边信号Type l feedback system l 型反馈系统UUint impulse response 单位冲激响应Uint ramp function 单位斜坡函数Undamped natural frequency 无阻尼自然相应Undamped system 无阻尼系统Underdamped systems 欠阻尼系统Undersampling 欠采样Unilateral 单边的Unilateral Laplace transform 单边拉普拉斯变换Unilateral z-transform 单边z变换Unit circle 单位圆Unit delay 单位延迟Unit doublets 单位冲激偶Unit impulse 单位冲激Unit step functions 单位阶跃函数Unit step response 单位阶跃响应Unstable systems 不稳定系统Unwrapped phase 展开的相位特性Upsampling 增采样VVariable 变量WWalsh functions 沃尔什函数Wave 波形Wavelengths 波长Weighted average 加权平均Wideband 宽带Wideband frequency modulation 宽带频率调制Windowing 加窗zZ domain z域Zero force equalizer 置零均衡器Zero-Input response 零输入响应Zero-Order hold 零阶保持Zeros of Laplace transform 拉普拉斯变换的零点Zero-state response 零状态响应z-transform z变换z-transform pairs z变换对。
神经网络（NeuralNetwork）
神经⽹络（NeuralNetwork）⼀、激活函数激活函数也称为响应函数，⽤于处理神经元的输出，理想的激活函数如阶跃函数，Sigmoid函数也常常作为激活函数使⽤。
在阶跃函数中，1表⽰神经元处于兴奋状态，0表⽰神经元处于抑制状态。
⼆、感知机感知机是两层神经元组成的神经⽹络，感知机的权重调整⽅式如下所⽰：按照正常思路w i+△w i是正常y的取值，w i是y'的取值，所以两者做差，增减性应当同(y-y')x i⼀致。
参数η是⼀个取值区间在(0,1)的任意数，称为学习率。
如果预测正确，感知机不发⽣变化，否则会根据错误的程度进⾏调整。
不妨这样假设⼀下，预测值不准确，说明Δw有偏差，⽆理x正负与否，w的变化应当和(y-y')x i⼀致，分情况讨论⼀下即可，x为负数，当预测值增加的时候，权值应当也增加，⽤来降低预测值，当预测值减少的时候，权值应当也减少，⽤来提⾼预测值；x为正数，当预测值增加的时候，权值应当减少，⽤来降低预测值，反之亦然。
(y-y')是出现的误差，负数对应下调，正数对应上调，乘上基数就是调整情况，因为基数的正负不影响调整情况，毕竟负数上调需要减少w的值。
感知机只有输出层神经元进⾏激活函数处理，即只拥有⼀层功能的神经元，其学习能⼒可以说是⾮常有限了。
如果对于两参数据，他们是线性可分的，那么感知机的学习过程会逐步收敛，但是对于线性不可分的问题，学习过程将会产⽣震荡，不断地左右进⾏摇摆，⽽⽆法恒定在⼀个可靠地线性准则中。
三、多层⽹络使⽤多层感知机就能够解决线性不可分的问题，输出层和输⼊层之间的成为隐层/隐含层，它和输出层⼀样都是拥有激活函数的功能神经元。
神经元之间不存在同层连接，也不存在跨层连接，这种神经⽹络结构称为多层前馈神经⽹络。
换⾔之，神经⽹络的训练重点就是链接权值和阈值当中。
四、误差逆传播算法误差逆传播算法换⾔之BP(BackPropagation)算法，BP算法不仅可以⽤于多层前馈神经⽹络，还可以⽤于其他⽅⾯，但是单单提起BP算法，训练的⾃然是多层前馈神经⽹络。
AI术语
人工智能专业重要词汇表1、A开头的词汇：Artificial General Intelligence/AGI通用人工智能Artificial Intelligence/AI人工智能Association analysis关联分析Attention mechanism注意力机制Attribute conditional independence assumption属性条件独立性假设Attribute space属性空间Attribute value属性值Autoencoder自编码器Automatic speech recognition自动语音识别Automatic summarization自动摘要Average gradient平均梯度Average-Pooling平均池化Accumulated error backpropagation累积误差逆传播Activation Function激活函数Adaptive Resonance Theory/ART自适应谐振理论Addictive model加性学习Adversarial Networks对抗网络Affine Layer仿射层Affinity matrix亲和矩阵Agent代理/ 智能体Algorithm算法Alpha-beta pruningα-β剪枝Anomaly detection异常检测Approximation近似Area Under ROC Curve／AUC R oc 曲线下面积2、B开头的词汇Backpropagation Through Time通过时间的反向传播Backpropagation/BP反向传播Base learner基学习器Base learning algorithm基学习算法Batch Normalization/BN批量归一化Bayes decision rule贝叶斯判定准则Bayes Model Averaging／BMA贝叶斯模型平均Bayes optimal classifier贝叶斯最优分类器Bayesian decision theory贝叶斯决策论Bayesian network贝叶斯网络Between-class scatter matrix类间散度矩阵Bias偏置/ 偏差Bias-variance decomposition偏差-方差分解Bias-Variance Dilemma偏差–方差困境Bi-directional Long-Short Term Memory/Bi-LSTM双向长短期记忆Binary classification二分类Binomial test二项检验Bi-partition二分法Boltzmann machine玻尔兹曼机Bootstrap sampling自助采样法／可重复采样／有放回采样Bootstrapping自助法Break-Event Point／BEP平衡点3、C开头的词汇Calibration校准Cascade-Correlation级联相关Categorical attribute离散属性Class-conditional probability类条件概率Classification and regression tree/CART分类与回归树Classifier分类器Class-imbalance类别不平衡Closed -form闭式Cluster簇/类/集群Cluster analysis聚类分析Clustering聚类Clustering ensemble聚类集成Co-adapting共适应Coding matrix编码矩阵COLT国际学习理论会议Committee-based learning基于委员会的学习Competitive learning竞争型学习Component learner组件学习器Comprehensibility可解释性Computation Cost计算成本Computational Linguistics计算语言学Computer vision计算机视觉Concept drift概念漂移Concept Learning System /CLS概念学习系统Conditional entropy条件熵Conditional mutual information条件互信息Conditional Probability Table／CPT条件概率表Conditional random field/CRF条件随机场Conditional risk条件风险Confidence置信度Confusion matrix混淆矩阵Connection weight连接权Connectionism连结主义Consistency一致性／相合性Contingency table列联表Continuous attribute连续属性Convergence收敛Conversational agent会话智能体Convex quadratic programming凸二次规划Convexity凸性Convolutional neural network/CNN卷积神经网络Co-occurrence同现Correlation coefficient相关系数Cosine similarity余弦相似度Cost curve成本曲线Cost Function成本函数Cost matrix成本矩阵Cost-sensitive成本敏感Cross entropy交叉熵Cross validation交叉验证Crowdsourcing众包Curse of dimensionality维数灾难Cut point截断点Cutting plane algorithm割平面法4、D开头的词汇Data mining数据挖掘Data set数据集Decision Boundary决策边界Decision stump决策树桩Decision tree决策树／判定树Deduction演绎Deep Belief Network深度信念网络Deep Convolutional Generative Adversarial Network/DCGAN深度卷积生成对抗网络Deep learning深度学习Deep neural network/DNN深度神经网络Deep Q-Learning深度Q 学习Deep Q-Network深度Q 网络Density estimation密度估计Density-based clustering密度聚类Differentiable neural computer可微分神经计算机Dimensionality reduction algorithm降维算法Directed edge有向边Disagreement measure不合度量Discriminative model判别模型Discriminator判别器Distance measure距离度量Distance metric learning距离度量学习Distribution分布Divergence散度Diversity measure多样性度量／差异性度量Domain adaption领域自适应Downsampling下采样D-separation （Directed separation）有向分离Dual problem对偶问题Dummy node哑结点Dynamic Fusion动态融合Dynamic programming动态规划5、E开头的词汇Eigenvalue decomposition特征值分解Embedding嵌入Emotional analysis情绪分析Empirical conditional entropy经验条件熵Empirical entropy经验熵Empirical error经验误差Empirical risk经验风险End-to-End端到端Energy-based model基于能量的模型Ensemble learning集成学习Ensemble pruning集成修剪Error Correcting Output Codes／ECOC纠错输出码Error rate错误率Error-ambiguity decomposition误差-分歧分解Euclidean distance欧氏距离Evolutionary computation演化计算Expectation-Maximization期望最大化Expected loss期望损失Exploding Gradient Problem梯度爆炸问题Exponential loss function指数损失函数Extreme Learning Machine/ELM超限学习机6、F开头的词汇Factorization因子分解False negative假负类False positive假正类False Positive Rate/FPR假正例率Feature engineering特征工程Feature selection特征选择Feature vector特征向量Featured Learning特征学习Feedforward Neural Networks/FNN前馈神经网络Fine-tuning微调Flipping output翻转法Fluctuation震荡Forward stagewise algorithm前向分步算法Frequentist频率主义学派Full-rank matrix满秩矩阵Functional neuron功能神经元7、G开头的词汇Gain ratio增益率Game theory博弈论Gaussian kernel function高斯核函数Gaussian Mixture Model高斯混合模型General Problem Solving通用问题求解Generalization泛化Generalization error泛化误差Generalization error bound泛化误差上界Generalized Lagrange function广义拉格朗日函数Generalized linear model广义线性模型Generalized Rayleigh quotient广义瑞利商Generative Adversarial Networks/GAN生成对抗网络Generative Model生成模型Generator生成器Genetic Algorithm/GA遗传算法Gibbs sampling吉布斯采样Gini index基尼指数Global minimum全局最小Global Optimization全局优化Gradient boosting梯度提升Gradient Descent梯度下降Graph theory图论Ground-truth真相／真实8、H开头的词汇Hard margin硬间隔Hard voting硬投票Harmonic mean调和平均Hesse matrix海塞矩阵Hidden dynamic model隐动态模型Hidden layer隐藏层Hidden Markov Model/HMM隐马尔可夫模型Hierarchical clustering层次聚类Hilbert space希尔伯特空间Hinge loss function合页损失函数Hold-out留出法Homogeneous同质Hybrid computing混合计算Hyperparameter超参数Hypothesis假设Hypothesis test假设验证9、I开头的词汇ICML国际机器学习会议Improved iterative scaling/IIS改进的迭代尺度法Incremental learning增量学习Independent and identically distributed/i.i.d.独立同分布Independent Component Analysis/ICA独立成分分析Indicator function指示函数Individual learner个体学习器Induction归纳Inductive bias归纳偏好Inductive learning归纳学习Inductive Logic Programming／ILP归纳逻辑程序设计Information entropy信息熵Information gain信息增益Input layer输入层Insensitive loss不敏感损失Inter-cluster similarity簇间相似度International Conference for Machine Learning/ICML国际机器学习大会Intra-cluster similarity簇内相似度Intrinsic value固有值Isometric Mapping/Isomap等度量映射Isotonic regression等分回归Iterative Dichotomiser迭代二分器10、K开头的词汇Kernel method核方法Kernel trick核技巧Kernelized Linear Discriminant Analysis／KLDA核线性判别分析K-fold cross validation k 折交叉验证／k 倍交叉验证K-Means Clustering K –均值聚类K-Nearest Neighbours Algorithm/KNN K近邻算法Knowledge base知识库Knowledge Representation知识表征11、L开头的词汇Label space标记空间Lagrange duality拉格朗日对偶性Lagrange multiplier拉格朗日乘子Laplace smoothing拉普拉斯平滑Laplacian correction拉普拉斯修正Latent Dirichlet Allocation隐狄利克雷分布Latent semantic analysis潜在语义分析Latent variable隐变量Lazy learning懒惰学习Learner学习器Learning by analogy类比学习Learning rate学习率Learning Vector Quantization/LVQ学习向量量化Least squares regression tree最小二乘回归树Leave-One-Out/LOO留一法linear chain conditional random field线性链条件随机场Linear Discriminant Analysis／LDA线性判别分析Linear model线性模型Linear Regression线性回归Link function联系函数Local Markov property局部马尔可夫性Local minimum局部最小Log likelihood对数似然Log odds／logit对数几率Logistic Regression Logistic 回归Log-likelihood对数似然Log-linear regression对数线性回归Long-Short Term Memory/LSTM长短期记忆Loss function损失函数12、M开头的词汇Machine translation/MT机器翻译Macron-P宏查准率Macron-R宏查全率Majority voting绝对多数投票法Manifold assumption流形假设Manifold learning流形学习Margin theory间隔理论Marginal distribution边际分布Marginal independence边际独立性Marginalization边际化Markov Chain Monte Carlo/MCMC马尔可夫链蒙特卡罗方法Markov Random Field马尔可夫随机场Maximal clique最大团Maximum Likelihood Estimation/MLE极大似然估计／极大似然法Maximum margin最大间隔Maximum weighted spanning tree最大带权生成树Max-Pooling最大池化Mean squared error均方误差Meta-learner元学习器Metric learning度量学习Micro-P微查准率Micro-R微查全率Minimal Description Length/MDL最小描述长度Minimax game极小极大博弈Misclassification cost误分类成本Mixture of experts混合专家Momentum动量Moral graph道德图／端正图Multi-class classification多分类Multi-document summarization多文档摘要Multi-layer feedforward neural networks多层前馈神经网络Multilayer Perceptron/MLP多层感知器Multimodal learning多模态学习Multiple Dimensional Scaling多维缩放Multiple linear regression多元线性回归Multi-response Linear Regression ／MLR多响应线性回归Mutual information互信息13、N开头的词汇Naive bayes朴素贝叶斯Naive Bayes Classifier朴素贝叶斯分类器Named entity recognition命名实体识别Nash equilibrium纳什均衡Natural language generation/NLG自然语言生成Natural language processing自然语言处理Negative class负类Negative correlation负相关法Negative Log Likelihood负对数似然Neighbourhood Component Analysis/NCA近邻成分分析Neural Machine Translation神经机器翻译Neural Turing Machine神经图灵机Newton method牛顿法NIPS国际神经信息处理系统会议No Free Lunch Theorem／NFL没有免费的午餐定理Noise-contrastive estimation噪音对比估计Nominal attribute列名属性Non-convex optimization非凸优化Nonlinear model非线性模型Non-metric distance非度量距离Non-negative matrix factorization非负矩阵分解Non-ordinal attribute无序属性Non-Saturating Game非饱和博弈Norm范数Normalization归一化Nuclear norm核范数Numerical attribute数值属性14、O开头的词汇Objective function目标函数Oblique decision tree斜决策树Occam’s razor奥卡姆剃刀Odds几率Off-Policy离策略One shot learning一次性学习One-Dependent Estimator／ODE独依赖估计On-Policy在策略Ordinal attribute有序属性Out-of-bag estimate包外估计Output layer输出层Output smearing输出调制法Overfitting过拟合／过配Oversampling过采样15、P开头的词汇Paired t-test成对t 检验Pairwise成对型Pairwise Markov property成对马尔可夫性Parameter参数Parameter estimation参数估计Parameter tuning调参Parse tree解析树Particle Swarm Optimization/PSO粒子群优化算法Part-of-speech tagging词性标注Perceptron感知机Performance measure性能度量Plug and Play Generative Network即插即用生成网络Plurality voting相对多数投票法Polarity detection极性检测Polynomial kernel function多项式核函数Pooling池化Positive class正类Positive definite matrix正定矩阵Post-hoc test后续检验Post-pruning后剪枝potential function势函数Precision查准率／准确率Prepruning预剪枝Principal component analysis/PCA主成分分析Principle of multiple explanations多释原则Prior先验Probability Graphical Model概率图模型Proximal Gradient Descent/PGD近端梯度下降Pruning剪枝Pseudo-label伪标记16、Q开头的词汇Quantized Neural Network量子化神经网络Quantum computer量子计算机Quantum Computing量子计算Quasi Newton method拟牛顿法17、R开头的词汇Radial Basis Function／RBF径向基函数Random Forest Algorithm随机森林算法Random walk随机漫步Recall查全率／召回率Receiver Operating Characteristic/ROC受试者工作特征Rectified Linear Unit/ReLU线性修正单元Recurrent Neural Network循环神经网络Recursive neural network递归神经网络Reference model参考模型Regression回归Regularization正则化Reinforcement learning/RL强化学习Representation learning表征学习Representer theorem表示定理reproducing kernel Hilbert space/RKHS再生核希尔伯特空间Re-sampling重采样法Rescaling再缩放Residual Mapping残差映射Residual Network残差网络Restricted Boltzmann Machine/RBM受限玻尔兹曼机Restricted Isometry Property/RIP限定等距性Re-weighting重赋权法Robustness稳健性/鲁棒性Root node根结点Rule Engine规则引擎Rule learning规则学习18、S开头的词汇Saddle point鞍点Sample space样本空间Sampling采样Score function评分函数Self-Driving自动驾驶Self-Organizing Map／SOM自组织映射Semi-naive Bayes classifiers半朴素贝叶斯分类器Semi-Supervised Learning半监督学习semi-Supervised Support Vector Machine半监督支持向量机Sentiment analysis情感分析Separating hyperplane分离超平面Sigmoid function Sigmoid 函数Similarity measure相似度度量Simulated annealing模拟退火Simultaneous localization and mapping同步定位与地图构建Singular Value Decomposition奇异值分解Slack variables松弛变量Smoothing平滑Soft margin软间隔Soft margin maximization软间隔最大化Soft voting软投票Sparse representation稀疏表征Sparsity稀疏性Specialization特化Spectral Clustering谱聚类Speech Recognition语音识别Splitting variable切分变量Squashing function挤压函数Stability-plasticity dilemma可塑性-稳定性困境Statistical learning统计学习Status feature function状态特征函Stochastic gradient descent随机梯度下降Stratified sampling分层采样Structural risk结构风险Structural risk minimization/SRM结构风险最小化Subspace子空间Supervised learning监督学习／有导师学习support vector expansion支持向量展式Support Vector Machine/SVM支持向量机Surrogat loss替代损失Surrogate function替代函数Symbolic learning符号学习Symbolism符号主义Synset同义词集19、T开头的词汇T-Distribution Stochastic Neighbour Embedding/t-SNE T–分布随机近邻嵌入Tensor张量Tensor Processing Units/TPU张量处理单元The least square method最小二乘法Threshold阈值Threshold logic unit阈值逻辑单元Threshold-moving阈值移动Time Step时间步骤Tokenization标记化Training error训练误差Training instance训练示例／训练例Transductive learning直推学习Transfer learning迁移学习Treebank树库Tria-by-error试错法True negative真负类True positive真正类True Positive Rate/TPR真正例率Turing Machine图灵机Twice-learning二次学习20、U开头的词汇Underfitting欠拟合／欠配Undersampling欠采样Understandability可理解性Unequal cost非均等代价Unit-step function单位阶跃函数Univariate decision tree单变量决策树Unsupervised learning无监督学习／无导师学习Unsupervised layer-wise training无监督逐层训练Upsampling上采样21、V开头的词汇Vanishing Gradient Problem梯度消失问题Variational inference变分推断VC Theory VC维理论Version space版本空间Viterbi algorithm维特比算法Von Neumann architecture冯·诺伊曼架构22、W开头的词汇Wasserstein GAN/WGAN Wasserstein生成对抗网络Weak learner弱学习器Weight权重Weight sharing权共享Weighted voting加权投票法Within-class scatter matrix类内散度矩阵Word embedding词嵌入Word sense disambiguation词义消歧23、Z开头的词汇Zero-data learning零数据学习Zero-shot learning零次学习。
caffe correlation算子原理
caffe correlation算子原理
Caffe是一个深度学习框架，它提供了一种用于计算图卷积的
操作，也称为correlation算子。
correlation算子是一种用于实
现特征匹配和相似度计算的卷积操作。
在Caffe中，correlation算子用于比较两个输入特征图之间的
相似度。
它通过计算输入特征图的每个位置的差异程度来实现。
算子的输入包括两个特征图和相关参数，输出是一个相似度矩阵。
具体而言，correlation算子通过在一个特征图上滑动一个固定
尺寸的窗口，并与另一个特征图上的对应窗口进行比较，计算两个窗口之间的差异程度。
这种差异程度可以使用相关性度量方法来计算，如互相关、归一化互相关等。
correlation算子的算法原理如下：
1. 为两个输入特征图选择相应的窗口大小和步幅。
2. 从第一个特征图的左上角开始滑动窗口，在与第二个特征图的对应位置进行比较。
3. 在两个窗口之间计算差异程度。
4. 将差异程度作为输出特征图的相应位置的值。
5. 重复2-4步骤，直到遍历完整个特征图。
correlation算子的结果是一个反映两个特征图相似度的特征图。
它可以用于实现各种计算任务，如光流估计、立体视觉、目标检测等。
需要注意的是，correlation算子在Caffe中只是一个操作，实际的计算是由底层的计算库来执行的，如cuDNN、cuBLAS 等。
这些库提供了高效的计算实现，以加速深度学习任务的执行。
Cascade-correlation级联相关 26页PPT文档
cascade-correlation
12
Algorithm
4. Try to maximize the correlation between the
activation of the candidate units and the residual error of the net by training all the links leading to a candidate unit. Learning takes place with an ordinary learning algorithm. The training is stopped when the correlation scores no longer improves.
Machine Learning, Neural and Statistical Classification by D. Michie, D.J. Spiegelhalter, C.C. Taylor (eds)
cascade-correlation
24
Thank you
cascade-correlation
Begins with some inputs and one or more outputs.
Every input is connected to every output. Bias is permanently set to +1.
cascade-correlation
6
Stage -1
2 CC starts with a minimal network consisting only of an input and an output layer. Both layers are fully connected
03-06 Correlation
3.6-20
Fitted Line Plot
出现下面的第二张 图面。
3.6-21
Fitted Line Plot
在options中不输入任 何值出现的图面。
} +8
} -8
在options中选择Display Prediction Bands时出现 的图面。
– – – – 杂质导致发泡 杂质导致低收益 增加压力来减少发泡 压力对发泡有反作用，但与收益毫不相干
3.6-17
简单回归
相关告诉我们在两个变量之间有多少线性关联，而回归 则更精确地说明这种关联。 具有一个（或多个）变量的方程的回归结果帮助解释在 另外一个变量中的散布
•
Stat>Regression>Regression
Output
70 Y = 99.1754 - 0.745022X 60 R-Squared = 0.876 50 40 30
Moderate Negative Correlation
110
100
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90
Input
Output
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Weak Negative Correlation
• 我们期望的相关性是什么？ • 利用 Stat>Basic Statistics>Correlation 程 序来研究其相关性
– 为什么这个相关不是我们所期待的？ – 让我们用分类相关系数画 histogram图
• 利用 Stat>Regression>Fitted Line Plot 程序来研究r-平方
Cascadecorrlation级联相关-精选文档
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Train stage 1. If error is not acceptable, proceed. Train stage 2. If error is not acceptable proceed.
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CC combines 2 key ideas.
Cascade architecture. Learning alogorithm.
cascade-correlation
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Cascade Architecture
cascade-correlation
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Algorithm
4. Try to maximize the correlation between the
activation of the candidate units and the residual error of the net by training all the links leading to a candidate unit. Learning takes place with an ordinary learning algorithm. The training is stopped when the correlation scores no longer improves.
相关（CORRELATION）的意义
Lecture 7 相關--兩變數間的關係(2-1)一、相關(Correlation)的意義二、相關係數的求法1.皮爾遜積差相關係數(Pearson product-moment correlation coefficient)1)共變(covary)2)共變數(covariance)--非標準化的關聯指數2.斯比爾曼等級相關(Spearman Rank Correlation)三、相關係數的含義1.方向--正負2.強度3.分佈圖(plot)四、相關係數的解釋1.直線關係2.非因果關係3.非百分比--解釋力4.與樣本大小有關--相關係數的檢定五、影響相關係數之因素1.直線轉換2.全距--同質團體3.不同團體資料之合併4.極端組之分配六、作業（六）1.將例一中之數學成績各加10分，其相關係數為何?是否改變?2.將例一中之數學成績各乘以2，其相關係數為何?是否改變?3.求下列資料之積差相關與等級相關───────X Y───────75 6686 76 63 61 82 73 79 69 ───────Lecture 7 相關(Correlation)--兩變數間的關係(2-2)例一:────────────────────────────────────────OBS 數學(X) 智力(Y) X-X Y-Y (X-X)2 (Y-Y)2 (X-X)(Y-Y)────────────────────────────────────────1 80 115 25 20 625 400 5002 75 110 20 15 400 225 3003 60 90 5 -5 25 25 -254 50 105 -5 10 25 100 -505 35 80 -20 -15 400 225 3006 30 70 -25 -25 625 625 625────────────────────────────────────────sum 330 570 2100 1600 1650M 55 95S 18.71 16.33智力 |(Y) |115 + *110 + *105 + *100 +95 +90 + *85 +80 + *75 +70 +*|-+------+------+------+------+------+------+------+------+------+--- 30 35 40 45 50 55 60 65 70 75 80數學(X)例二:────────────────────────X Y Rx Ry Rx-Ry(D) D*D────────────────────────53 51 1 5 -4 1650 56 2 4 -2 448 59 3 2 1 147 48 4 6 -2 446 37 5 9 -4 1645 25 6 11 -5 2544 58 7 3 4 1643 44 8 8 0 042 34 9 10 -1 140 67 10 1 9 8135 46 11 7 4 16──────────────────────── 180。
Visual_studio术语中英对照
break mode 中断模式
break state 中断状态
Bring Forward 上移一层
Bring In Front 置前
Bring to Front 置于顶层
broker 中间装置
browsable 可浏览
Browse With 浏览方式
Attributes Property Attributes 属性
Authentication 身份验证
authorable 可创作（的）
Auto completion for commands 自动完成命令
Auto Increment 自动增加
Auto Syntax Check 自动语法校验
bitwise OR operator 位 OR 运算符
block 块
block if If 块
blocking UI 模块化用户界面
body 体
Book Edition 试用版
bookmark 书签
Boolean 布尔型；布尔值
bottom margin 下边距
abort 中止
abstract class 抽象类
accelerator 快捷键
accelerator mapping 快捷键映射
accelerator table 快捷键对应表
access modifier 访问修饰符
Access Pack 访问包
access specifier 访问说明符
class object 类对象
class type 类类型
Class View Group By Type 类视图“按类型分组”
典型关联分析
典型关联分析（Canonical Correlation Analysis）[pdf版本]典型相关分析.pdf1. 问题在线性回归中，我们使用直线来拟合样本点，寻找n维特征向量X和输出结果（或者叫做label）Y之间的线性关系。
其中，。
然而当Y也是多维时，或者说Y也有多个特征时，我们希望分析出X和Y的关系。
当然我们仍然可以使用回归的方法来分析，做法如下：假设，，那么可以建立等式Y=AX如下其中，形式和线性回归一样，需要训练m次得到m个。
这样做的一个缺点是，Y中的每个特征都与X的所有特征关联，Y中的特征之间没有什么联系。
我们想换一种思路来看这个问题，如果将X和Y都看成整体，考察这两个整体之间的关系。
我们将整体表示成X和Y各自特征间的线性组合，也就是考察和之间的关系。
这样的应用其实很多，举个简单的例子。
我们想考察一个人解题能力X（解题速度，解题正确率）与他/她的阅读能力Y（阅读速度，理解程度）之间的关系，那么形式化为：和然后使用Pearson相关系数来度量u和v的关系，我们期望寻求一组最优的解a和b，使得Corr(u, v)最大，这样得到的a和b就是使得u和v就有最大关联的权重。
到这里，基本上介绍了典型相关分析的目的。
2. CCA表示与求解给定两组向量和（替换之前的x为，y为），维度为，维度为，默认。
形式化表示如下：是x的协方差矩阵；左上角是自己的协方差矩阵；右上角是；左下角是，也是的转置；右下角是的协方差矩阵。
与之前一样，我们从和的整体入手，定义我们可以算出u和v的方差和协方差：上面的结果其实很好算，推导一下第一个吧：最后，我们需要算Corr(u,v)了我们期望Corr(u,v)越大越好，关于Pearson相关系数，《数据挖掘导论》给出了一个很好的图来说明：MaximizeSubject to:求解方法是构造Lagrangian等式，这里我简单推导如下：求导，得令导数为0后，得到方程组：第一个等式左乘，第二个左乘，再根据，得到也就是说求出的即是Corr(u,v)，只需找最大即可。
Cascade-correlation级联相关27页PPT
15
Example -Two spirals problem
x0 y1
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y2 x2
cascade-correlation
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Train stage 1. If scade-correlation
14
Algorithm
6. To change the candidate unit into a hidden unit,
generate links between the selected unit and all the output units. Since the weights leading to the new hidden unit are frozen, a new permanent
2 CC starts with a minimal network consisting only of an input and an output layer. Both layers are fully connected
cascade-correlation
11
Algorithm
3. Generate the so-called candidate units. Every candidate unit is connected with all input units and with all existing hidden units. Between the pool of candidate units and the output units there are no weights.
LA CASCADELa级联
Tout n’est cependant pas permis Le vétérinaire est pleinement responsable de sa
4) une préparation magistrale ( = préparation extemporanée préparée sur prescription vétérinaire)
Interprétation
1. Médicament autorisé 2. Médicament approprié 3. Médicament disponible
Choix d’un médicament
A.M.M.
- Principe actif - Forme galéation
Indication thérapeutique
Temps d’attente
Prescription du vétérinaire
une AMM répondant aux mêmes critères précédemment définis
La Cascade - L. 5143-4
En l ’absence de médicament autorisé approprié disponible, le vétérinaire peut
Médicament autorisé
A.M.M. (L. 5141-5 du CSP)
Délivrée par l’AFSSA
A.T.U. (L. 5141-10 du CSP) = Autorisation Temporaire d’Utilisation
Cascade-correlation级联相关
cascade-correlation
14
Algorithm
6. To change the candidate unit into a hidden unit,
generate links between the selected unit and all the output units. Since the weights leading to the new hidden unit are frozen, a new permanent
Cascade-correlation
By Kranthi & sudhan
cascade-correlation
1
Contents
Motivation Recollecting back-prop Cascading architecture Learning Algorithm Example Comparision with other systems
cascade-correlation
2
Motivation
Curse of dimensionality Simple network Determines the structure Fast learner
cascade-correlation
3
Recollect
What`s Back-propagation? Problems with this alogorithm. How CC is going to solve these problems?
weights in network so results can be cached.
cascade-correlation
Cascadecorrlation级联相关
Etc
cascade-correlation
10
Algorithm
1. Train all the connections ending at an output unit with a usual learning algorithm until the error of the net no longer decreases.
weights in network so results can be cached.
cascade-correlation
19
Experimental results for hand written digits data sets
cascade-correlation
20
Experimental results for the patients with severe head injury dataset
Cascade-correlation
By Kranthi & sudhan
cascade-correlation
1
Contents
Motivation Recollecting back-prop Cascading architecture Learning Algorithm Example Comparision with other systems
cascade-correlation
12
Algorithm
4. Try to maximize the correlation between the
CAMPAGNE CASCADE简洁的级联
Wind-driven mixing and cooling of the upper layer of the ocean during winter
Cascading : Down-slope cascade of dense water formed along the coast
Moorings
CTD station and transect
Current, turbidity, and particle fluxes
ADCP current speed (cm/s)
ADCP Echo amplitude (count)
Large current (up to 90 cm/s), turbidity (up to 25 mg/l), and particles fluxes
Mixing
ห้องสมุดไป่ตู้
Radiale L
General circulation and winds combine to predispose some part of a gyre to locally overturn
Sinking
Heat loss associated with the wind exceed some critical level and initiates convection
Preliminary Results of the CASCADE Cruise (Gulf of Lions)
CASCADE: CAscading, Storm, Convection, Advection and Downwelling Events
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15
Example -Two spirals problem
Train stage 2. If error is not acceptable proceed.
Etc
cascade-correlation
10
Algorithm
1. Train all the connections ending at an output unit with a usual learning algorithm until the error of the net no longer decreases.
cascade-correlation
14
Algorithm
6. To change the candidate unit into a hidden unit,
generate links between the selected unit and all the output units. Since the weights leading to the new hidden unit are frozen, a new permanent
cascade-correlation
13
Algorithm
In order to maximize this S,we compute partial derivative of S with respect to each candidate units incoming weight
5.Choose the candidate unit with the maximum correlation, freeze its incoming weights and add it to the net.
Cascade-correlation
By Kranthi & sudhan
cascade-correlation
1
Contents
Motivation Recollecting back-prop Cascading architecture Learning Algorithm Example Comparision with other systems
Begins with some inputs and one or more outputs.
Every input is connected to every output. Bias is permanently set to +1.
cascade-correlation
6
Stage -1
cascade-correlation
2
Motivation
Curse of dimensionality Simple network Determines the structure Fast learner
cascade-correlation
3
Recollect
What`s Back-propagation? Problems with this alogorithm. How CC is going to solve these problems?
x0 y1
x1
y2 x2
cascade-correla
x1
z1
y2
x2
cascade-correlation
8
Stage-3
x0 y1
x1
z1
z2
y2
x2
cascade-correlation
9
Algorithm
Train stage 1. If error is not acceptable, proceed.
2 CC starts with a minimal network consisting only of an input and an output layer. Both layers are fully connected
cascade-correlation
11
Algorithm
3. Generate the so-called candidate units. Every candidate unit is connected with all input units and with all existing hidden units. Between the pool of candidate units and the output units there are no weights.
cascade-correlation
4
Cascade-Correlation
CC combines 2 key ideas.
Cascade architecture. Learning alogorithm.
cascade-correlation
5
Cascade Architecture
feature detector is obtained. Loop back to step 2.
7. This algorithm is repeated until the overall error of the net falls below a given value
cascade-correlation
cascade-correlation
12
Algorithm
4. Try to maximize the correlation between the
activation of the candidate units and the residual error of the net by training all the links leading to a candidate unit. Learning takes place with an ordinary learning algorithm. The training is stopped when the correlation scores no longer improves.