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1.

図書

図書
Richard O. Duda, Peter E. Hart
出版情報: New York : Wiley, c1973  xvii, 482 p. ; 24 cm
シリーズ名: A Wiley-Interscience publication
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2.

図書

図書
edited by J.K. Aggarwal, Richard O. Duda, Azriel Rosenfeld
出版情報: [New York] : IEEE Press : sole worldwide distributor (exclusive of IEEE) Wiley, c1977  vi, 466 p. ; 28 cm
シリーズ名: IEEE Press selected reprint series
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3.

図書

図書
Richard O.Duda, Peter E.Hart, David G.Stork著 ; 尾上守夫監訳
出版情報: 東京 : 新技術コミュニケーションズ, 2007.5  xviii, 652p ; 27cm
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4.

図書

図書
Richard O. Duda, Peter E. Hart, David G. Stork
出版情報: New York ; Chichester : Wiley, c2001  xx, 654 p. ; 27 cm
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目次情報: 続きを見る
Preface
Introduction / 1:
Machine Perception / 1.1:
An Example / 1.2:
Related Fields / 1.2.1:
Pattern Recognition Systems / 1.3:
Sensing / 1.3.1:
Segmentation and Grouping / 1.3.2:
Feature Extraction / 1.3.3:
Classification / 1.3.4:
Post Processing / 1.3.5:
The Design Cycle / 1.4:
Data Collection / 1.4.1:
Feature Choice / 1.4.2:
Model Choice / 1.4.3:
Training / 1.4.4:
Evaluation / 1.4.5:
Computational Complexity / 1.4.6:
Learning and Adaptation / 1.5:
Supervised Learning / 1.5.1:
Unsupervised Learning / 1.5.2:
Reinforcement Learning / 1.5.3:
Conclusion / 1.6:
Summary by Chapters
Bibliographical and Historical Remarks
Bibliography
Bayesian Decision Theory / 2:
Bayesian Decision Theory--Continuous Features / 2.1:
Two-Category Classification / 2.2.1:
Minimum-Error-Rate Classification / 2.3:
Minimax Criterion / 2.3.1:
Neyman-Pearson Criterion / 2.3.2:
Classifiers, Discriminant Functions, and Decision Surfaces / 2.4:
The Multicategory Case / 2.4.1:
The Two-Category Case / 2.4.2:
The Normal Density / 2.5:
Univariate Density / 2.5.1:
Multivariate Density / 2.5.2:
Discriminant Functions for the Normal Density / 2.6:
Case 1: [Sigma subscript i] = [sigma superscript 2]I / 2.6.1:
Case 2: [Sigma ubscript i] = [Sigma] / 2.6.2:
Case 3: [Sigma subscript i] = arbitrary / 2.6.3:
Decision Regions for Two-Dimensional Gaussian Data / Example 1:
Error Probabilities and Integrals / 2.7:
Error Bounds for Normal Densities / 2.8:
Chernoff Bound / 2.8.1:
Bhattacharyya Bound / 2.8.2:
Error Bounds for Gaussian Distributions / Example 2:
Signal Detection Theory and Operating Characteristics / 2.8.3:
Bayes Decision Theory--Discrete Features / 2.9:
Independent Binary Features / 2.9.1:
Bayesian Decisions for Three-Dimensional Binary Data / Example 3:
Missing and Noisy Features / 2.10:
Missing Features / 2.10.1:
Noisy Features / 2.10.2:
Bayesian Belief Networks / 2.11:
Belief Network for Fish / Example 4:
Compound Bayesian Decision Theory and Context / 2.12:
Summary
Problems
Computer exercises
Maximum-Likelihood and Bayesian Parameter Estimation / 3:
Maximum-Likelihood Estimation / 3.1:
The General Principle / 3.2.1:
The Gaussian Case: Unknown [mu] / 3.2.2:
The Gaussian Case: Unknown [mu] and [Sigma] / 3.2.3:
Bias / 3.2.4:
Bayesian Estimation / 3.3:
The Class-Conditional Densities / 3.3.1:
The Parameter Distribution / 3.3.2:
Bayesian Parameter Estimation: Gaussian Case / 3.4:
The Univariate Case: p([mu]|D) / 3.4.1:
The Univariate Case: p(x|D) / 3.4.2:
The Multivariate Case / 3.4.3:
Bayesian Parameter Estimation: General Theory / 3.5:
Recursive Bayes Learning
When Do Maximum-Likelihood and Bayes Methods Differ? / 3.5.1:
Noninformative Priors and Invariance / 3.5.2:
Gibbs Algorithm / 3.5.3:
Sufficient Statistics / 3.6:
Sufficient Statistics and the Exponential Family / 3.6.1:
Problems of Dimensionality / 3.7:
Accuracy, Dimension, and Training Sample Size / 3.7.1:
Overfitting / 3.7.2:
Component Analysis and Discriminants / 3.8:
Principal Component Analysis (PCA) / 3.8.1:
Fisher Linear Discriminant / 3.8.2:
Multiple Discriminant Analysis / 3.8.3:
Expectation-Maximization (EM) / 3.9:
Expectation-Maximization for a 2D Normal Model
Hidden Markov Models / 3.10:
First-Order Markov Models / 3.10.1:
First-Order Hidden Markov Models / 3.10.2:
Hidden Markov Model Computation / 3.10.3:
Hidden Markov Model / 3.10.4:
Decoding / 3.10.5:
HMM Decoding
Learning / 3.10.6:
Nonparametric Techniques / 4:
Density Estimation / 4.1:
Parzen Windows / 4.3:
Convergence of the Mean / 4.3.1:
Convergence of the Variance / 4.3.2:
Illustrations / 4.3.3:
Classification Example / 4.3.4:
Probabilistic Neural Networks (PNNs) / 4.3.5:
Choosing the Window Function / 4.3.6:
k[subscript n]-Nearest-Neighbor Estimation / 4.4:
k[subscript n]-Nearest-Neighbor and Parzen-Window Estimation / 4.4.1:
Estimation of A Posteriori Probabilities / 4.4.2:
The Nearest-Neighbor Rule / 4.5:
Convergence of the Nearest Neighbor / 4.5.1:
Error Rate for the Nearest-Neighbor Rule / 4.5.2:
Error Bounds / 4.5.3:
The k-Nearest-Neighbor Rule / 4.5.4:
Computational Complexity of the k-Nearest-Neighbor Rule / 4.5.5:
Metrics and Nearest-Neighbor Classification / 4.6:
Properties of Metrics / 4.6.1:
Tangent Distance / 4.6.2:
Fuzzy Classification / 4.7:
Reduced Coulomb Energy Networks / 4.8:
Approximations by Series Expansions / 4.9:
Linear Discriminant Functions / 5:
Linear Discriminant Functions and Decision Surfaces / 5.1:
Generalized Linear Discriminant Functions / 5.2.1:
The Two-Category Linearly Separable Case / 5.4:
Geometry and Terminology / 5.4.1:
Gradient Descent Procedures / 5.4.2:
Minimizing the Perceptron Criterion Function / 5.5:
The Perceptron Criterion Function / 5.5.1:
Convergence Proof for Single-Sample Correction / 5.5.2:
Some Direct Generalizations / 5.5.3:
Relaxation Procedures / 5.6:
The Descent Algorithm / 5.6.1:
Convergence Proof / 5.6.2:
Nonseparable Behavior / 5.7:
Minimum Squared-Error Procedures / 5.8:
Minimum Squared-Error and the Pseudoinverse / 5.8.1:
Constructing a Linear Classifier by Matrix Pseudoinverse
Relation to Fisher's Linear Discriminant / 5.8.2:
Asymptotic Approximation to an Optimal Discriminant / 5.8.3:
The Widrow-Hoff or LMS Procedure / 5.8.4:
Stochastic Approximation Methods / 5.8.5:
The Ho-Kashyap Procedures / 5.9:
The Descent Procedure / 5.9.1:
Some Related Procedures / 5.9.2:
Linear Programming Algorithms / 5.10:
Linear Programming / 5.10.1:
The Linearly Separable Case / 5.10.2:
Support Vector Machines / 5.10.3:
SVM Training / 5.11.1:
SVM for the XOR Problem
Multicategory Generalizations / 5.12:
Kesler's Construction / 5.12.1:
Convergence of the Fixed-Increment Rule / 5.12.2:
Generalizations for MSE Procedures / 5.12.3:
Multilayer Neural Networks / 6:
Feedforward Operation and Classification / 6.1:
General Feedforward Operation / 6.2.1:
Expressive Power of Multilayer Networks / 6.2.2:
Backpropagation Algorithm / 6.3:
Network Learning / 6.3.1:
Training Protocols / 6.3.2:
Learning Curves / 6.3.3:
Error Surfaces / 6.4:
Some Small Networks / 6.4.1:
The Exclusive-OR (XOR) / 6.4.2:
Larger Networks / 6.4.3:
How Important Are Multiple Minima? / 6.4.4:
Backpropagation as Feature Mapping / 6.5:
Representations at the Hidden Layer--Weights / 6.5.1:
Backpropagation, Bayes Theory and Probability / 6.6:
Bayes Discriminants and Neural Networks / 6.6.1:
Outputs as Probabilities / 6.6.2:
Related Statistical Techniques / 6.7:
Practical Techniques for Improving Backpropagation / 6.8:
Activation Function / 6.8.1:
Parameters for the Sigmoid / 6.8.2:
Scaling Input / 6.8.3:
Target Values / 6.8.4:
Training with Noise / 6.8.5:
Manufacturing Data / 6.8.6:
Number of Hidden Units / 6.8.7:
Initializing Weights / 6.8.8:
Learning Rates / 6.8.9:
Momentum / 6.8.10:
Weight Decay / 6.8.11:
Hints / 6.8.12:
On-Line, Stochastic or Batch Training? / 6.8.13:
Stopped Training / 6.8.14:
Number of Hidden Layers / 6.8.15:
Criterion Function / 6.8.16:
Second-Order Methods / 6.9:
Hessian Matrix / 6.9.1:
Newton's Method / 6.9.2:
Quickprop / 6.9.3:
Conjugate Gradient Descent / 6.9.4:
Additional Networks and Training Methods / 6.10:
Radial Basis Function Networks (RBFs) / 6.10.1:
Special Bases / 6.10.2:
Matched Filters / 6.10.3:
Convolutional Networks / 6.10.4:
Recurrent Networks / 6.10.5:
Cascade-Correlation / 6.10.6:
Regularization, Complexity Adjustment and Pruning / 6.11:
Stochastic Methods / 7:
Stochastic Search / 7.1:
Simulated Annealing / 7.2.1:
The Boltzmann Factor / 7.2.2:
Deterministic Simulated Annealing / 7.2.3:
Boltzmann Learning / 7.3:
Stochastic Boltzmann Learning of Visible States / 7.3.1:
Missing Features and Category Constraints / 7.3.2:
Deterministic Boltzmann Learning / 7.3.3:
Initialization and Setting Parameters / 7.3.4:
Boltzmann Networks and Graphical Models / 7.4:
Other Graphical Models / 7.4.1:
Evolutionary Methods / 7.5:
Genetic Algorithms / 7.5.1:
Further Heuristics / 7.5.2:
Why Do They Work? / 7.5.3:
Genetic Programming / 7.6:
Nonmetric Methods / 8:
Decision Trees / 8.1:
Cart / 8.3:
Number of Splits / 8.3.1:
Query Selection and Node Impurity / 8.3.2:
When to Stop Splitting / 8.3.3:
Pruning / 8.3.4:
Assignment of Leaf Node Labels / 8.3.5:
A Simple Tree
Multivariate Decision Trees / 8.3.6:
Priors and Costs / 8.3.9:
Missing Attributes / 8.3.10:
Surrogate Splits and Missing Attributes
Other Tree Methods / 8.4:
ID3 / 8.4.1:
C4.5 / 8.4.2:
Which Tree Classifier Is Best? / 8.4.3:
Recognition with Strings / 8.5:
String Matching / 8.5.1:
Edit Distance / 8.5.2:
String Matching with Errors / 8.5.3:
String Matching with the "Don't-Care" Symbol / 8.5.5:
Grammatical Methods / 8.6:
Grammars / 8.6.1:
Types of String Grammars / 8.6.2:
A Grammar for Pronouncing Numbers
Recognition Using Grammars / 8.6.3:
Grammatical Inference / 8.7:
Rule-Based Methods / 8.8:
Learning Rules / 8.8.1:
Algorithm-Independent Machine Learning / 9:
Lack of Inherent Superiority of Any Classifier / 9.1:
No Free Lunch Theorem / 9.2.1:
No Free Lunch for Binary Data
Ugly Duckling Theorem / 9.2.2:
Minimum Description Length (MDL) / 9.2.3:
Minimum Description Length Principle / 9.2.4:
Overfitting Avoidance and Occam's Razor / 9.2.5:
Bias and Variance / 9.3:
Bias and Variance for Regression / 9.3.1:
Bias and Variance for Classification / 9.3.2:
Resampling for Estimating Statistics / 9.4:
Jackknife / 9.4.1:
Jackknife Estimate of Bias and Variance of the Mode
Bootstrap / 9.4.2:
Resampling for Classifier Design / 9.5:
Bagging / 9.5.1:
Boosting / 9.5.2:
Learning with Queries / 9.5.3:
Arcing, Learning with Queries, Bias and Variance / 9.5.4:
Estimating and Comparing Classifiers / 9.6:
Parametric Models / 9.6.1:
Cross-Validation / 9.6.2:
Jackknife and Bootstrap Estimation of Classification Accuracy / 9.6.3:
Maximum-Likelihood Model Comparison / 9.6.4:
Bayesian Model Comparison / 9.6.5:
The Problem-Average Error Rate / 9.6.6:
Predicting Final Performance from Learning Curves / 9.6.7:
The Capacity of a Separating Plane / 9.6.8:
Combining Classifiers / 9.7:
Component Classifiers with Discriminant Functions / 9.7.1:
Component Classifiers without Discriminant Functions / 9.7.2:
Unsupervised Learning and Clustering / 10:
Mixture Densities and Identifiability / 10.1:
Maximum-Likelihood Estimates / 10.3:
Application to Normal Mixtures / 10.4:
Case 1: Unknown Mean Vectors / 10.4.1:
Case 2: All Parameters Unknown / 10.4.2:
k-Means Clustering / 10.4.3:
Fuzzy k-Means Clustering / 10.4.4:
Unsupervised Bayesian Learning / 10.5:
The Bayes Classifier / 10.5.1:
Learning the Parameter Vector / 10.5.2:
Unsupervised Learning of Gaussian Data
Decision-Directed Approximation / 10.5.3:
Data Description and Clustering / 10.6:
Similarity Measures / 10.6.1:
Criterion Functions for Clustering / 10.7:
The Sum-of-Squared-Error Criterion / 10.7.1:
Related Minimum Variance Criteria / 10.7.2:
Scatter Criteria / 10.7.3:
Clustering Criteria
Iterative Optimization / 10.8:
Hierarchical Clustering / 10.9:
Definitions / 10.9.1:
Agglomerative Hierarchical Clustering / 10.9.2:
Stepwise-Optimal Hierarchical Clustering / 10.9.3:
Hierarchical Clustering and Induced Metrics / 10.9.4:
The Problem of Validity / 10.10:
On-line clustering / 10.11:
Unknown Number of Clusters / 10.11.1:
Adaptive Resonance / 10.11.2:
Learning with a Critic / 10.11.3:
Graph-Theoretic Methods / 10.12:
Component Analysis / 10.13:
Nonlinear Component Analysis (NLCA) / 10.13.1:
Independent Component Analysis (ICA) / 10.13.3:
Low-Dimensional Representations and Multidimensional Scaling (MDS) / 10.14:
Self-Organizing Feature Maps / 10.14.1:
Clustering and Dimensionality Reduction / 10.14.2:
Mathematical Foundations / A:
Notation / A.1:
Linear Algebra / A.2:
Notation and Preliminaries / A.2.1:
Inner Product / A.2.2:
Outer Product / A.2.3:
Derivatives of Matrices / A.2.4:
Determinant and Trace / A.2.5:
Matrix Inversion / A.2.6:
Eigenvectors and Eigenvalues / A.2.7:
Lagrange Optimization / A.3:
Probability Theory / A.4:
Discrete Random Variables / A.4.1:
Expected Values / A.4.2:
Pairs of Discrete Random Variables / A.4.3:
Statistical Independence / A.4.4:
Expected Values of Functions of Two Variables / A.4.5:
Conditional Probability / A.4.6:
The Law of Total Probability and Bayes' Rule / A.4.7:
Vector Random Variables / A.4.8:
Expectations, Mean Vectors and Covariance Matrices / A.4.9:
Continuous Random Variables / A.4.10:
Distributions of Sums of Independent Random Variables / A.4.11:
Normal Distributions / A.4.12:
Gaussian Derivatives and Integrals / A.5:
Multivariate Normal Densities / A.5.1:
Bivariate Normal Densities / A.5.2:
Hypothesis Testing / A.6:
Chi-Squared Test / A.6.1:
Information Theory / A.7:
Entropy and Information / A.7.1:
Relative Entropy / A.7.2:
Mutual Information / A.7.3:
Index / A.8:
Preface
Introduction / 1:
Machine Perception / 1.1:
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