资源说明：Chapter 1. Introduction 1.1 Is Pattern Recognition Important? 1.2 Features, Feature Vectors, and Classifiers 1.3 Supervised, Unsupervised, and Semi-Supervised Learning 1.4 MATLAB Programs 1.5 Outline of the Book Chapter 2. Classifiers Based on Bayes Decision Theory 2.1 Introduction 2.2 Bayes Decision Theory 2.3 Discriminant Functions and Decision Surfaces 2.4 Bayesian Classification for Normal Distributions 2.5 Estimation of Unknown Probability Density Functions 2.6 The Nearest Neighbor Rule 2.7 Bayesian Networks 2.8 Problems References Chapter 3. Linear Classifiers 3.1 Introduction 3.2 Linear Discriminant Functions and Decision Hyperplanes 3.3 The Perceptron Algorithm 3.4 Least Squares Methods 3.5 Mean Square Estimation Revisited 3.6 Logistic Discrimination 3.7 Support Vector Machines 3.8 Problems MATLAB Programs and Exercises References Chapter 4. Nonlinear Classifiers 4.1 Introduction 4.2 The XOR Problem 4.3 The Two-Layer Perceptron 4.4 Three-Layer Perceptrons 4.5 Algorithms Based on Exact Classification of the Training Set 4.6 The Backpropagation Algorithm 4.7 Variations on the Backpropagation Theme 4.8 The Cost Function Choice 4.9 Choice of the Network Size 4.10 A Simulation Example 4.11 Networks with Weight Sharing 4.12 Generalized Linear Classifiers 4.13 Capacity of the l-Dimensional Space in Linear Dichotomies 4.14 Polynomial Classifiers 4.15 Radial Basis Function Networks 4.16 Universal Approximators 4.17 Probabilistic Neural Networks 4.18 Support Vector Machines: The Nonlinear Case 4.19 Beyond the SVM Paradigm 4.20 Decision Trees 4.21 Combining Classifiers 4.22 The Boosting Approach to Combine Classifiers 4.23 The Class Imbalance Problem 4.24 Discussion 4.25 Problems References Chapter 5. Feature Selection 5.1 Introduction 5.2 Preprocessing 5.3 The Peaking Phenomenon 5.4 Feature Selection Based on Statistical Hypothesis Testing 5.5 The Receiver Operating Characteristics (ROC) Curve 5.6 Class Separability Measures 5.7 Feature Subset Selection 5.8 Optimal Feature Generation 5.9 Neural Networks and Feature Generation/Selection 5.10 A Hint on Generalization Theory 5.11 The Bayesian Information Criterion 5.12 Problems MATLAB Programs and Exercises References Chapter 6. Feature Generation I: Data Transformation and Dimensionality Reduction 6.1 Introduction 6.2 Basis Vectors and Images 6.3 The Karhunen–Loève Transform 6.4 The Singular Value Decomposition 6.5 Independent Component Analysis 6.6 Nonnegative Matrix Factorization 6.7 Nonlinear Dimensionality Reduction 6.8 The Discrete Fourier Transform (DFT) 6.9 The Discrete Cosine and Sine Transforms 6.10 The Hadamard Transform 6.11 The Haar Transform 6.12 The Haar Expansion Revisited 6.13 Discrete Time Wavelet Transform (DTWT) 6.14 The Multiresolution Interpretation 6.15 Wavelet Packets 6.16 A Look at Two-Dimensional Generalizations 6.17 Applications 6.18 Problems MATLAB Programs and Exercises References Chapter 7. Feature Generation II 7.1 Introduction 7.2 Regional Features 7.3 Features for Shape and Size Characterization 7.4 A Glimpse at Fractals 7.5 Typical Features for Speech and Audio Classification 7.6 Problems MATLAB Programs and Exercises References Chapter 8. Template Matching 8.1 Introduction 8.2 Measures Based on Optimal Path Searching Techniques 8.3 Measures Based on Correlations 8.4 Deformable Template Models 8.5 Content-Based Information Retrieval: Relevance Feedback 8.6 Problems MATLAB Programs and Exercises References Chapter 9. Context-Dependent Classification 9.1 Introduction 9.2 The Bayes Classifier 9.3 Markov Chain Models 9.4 The Viterbi Algorithm 9.5 Channel Equalization 9.6 Hidden Markov Models 9.7 HMM with State Duration Modeling 9.8 Training Markov Models via Neural Networks 9.9 A Discussion of Markov Random Fields 9.10 Problems MATLAB Programs and Exercises References Chapter 10. Supervised Learning: The Epilogue 10.1 Introduction 10.2 Error-Counting Approach 10.3 Exploiting the Finite Size of the Data Set 10.4 A Case Study from Medical Imaging 10.5 Semi-Supervised Learning 10.6 Problems References Chapter 11. Clustering: Basic Concepts 11.1 Introduction 11.2 Proximity Measures 11.3 Problems References Chapter 12. Clustering Algorithms I: Sequential Algorithms 12.1 Introduction 12.2 Categories of Clustering Algorithms 12.3 Sequential Clustering Algorithms 12.4 A Modification of BSAS 12.5 A Two-Threshold Sequential Scheme 12.6 Refinement Stages 12.7 Neural Network Implementation 12.8 Problems MATLAB Programs and Exercises References Chapter 13. Clustering Algorithms II: Hierarchical Algorithms 13.1 Introduction 13.2 Agglomerative Algorithms 13.3 The Cophenetic Matrix 13.4 Divisive Algorithms 13.5 Hierarchical Algorithms for Large Data Sets 13.6 Choice of the Best Number of Clusters 13.7 Problems MATLAB Programs and Exercises References Chapter 14. Clustering Algorithms III: Schemes Based on Function Optimization 14.1 Introduction 14.2 Mixture Decomposition Schemes 14.3 Fuzzy Clustering Algorithms 14.4 Possibilistic Clustering 14.5 Hard Clustering Algorithms 14.6 Vector Quantization Appendix 14.7 Problems MATLAB Programs and Excercises References Chapter 15. Clustering Algorithms IV 15.1 Introduction 15.2 Clustering Algorithms Based on Graph Theory 15.3 Competitive Learning Algorithms 15.4 Binary Morphology Clustering Algorithms (BMCAs) 15.5 Boundary Detection Algorithms 15.6 Valley-Seeking Clustering Algorithms 15.7 Clustering via Cost Optimization (Revisited) 15.8 Kernel Clustering Methods 15.9 Density-Based Algorithms for Large Data Sets 15.10 Clustering Algorithms for High-Dimensional Data Sets 15.11 Other Clustering Algorithms 15.12 Combination of Clusterings 15.13 Problems MATLAB Programs and Exercises References Chapter 16. Cluster Validity 16.1 Introduction 16.2 Hypothesis Testing Revisited 16.3 Hypothesis Testing in Cluster Validity 16.4 Relative Criteria 16.5 Validity of Individual Clusters 16.6 Clustering Tendency 16.7 Problems References Appendix A. Hints from Probability and Statistics A.1 Total Probability and the Bayes Rule A.2 Mean and Variance A.3 Statistical Independence A.4 Marginalization A.5 Characteristic Functions A.6 Moments and Cumulants A.7 Edgeworth Expansion of a Pdf A.8 Kullback–Leibler Distance A.9 Multivariate Gaussian or Normal Probability Density Function A.10 Transformation of Random Variables A.11 The Cramer–Rao Lower Bound A.12 Central Limit Theorem A.13 Chi-Square Distribution A.14 t-Distribution A.15 Beta Distribution A.16 Poisson Distribution A.17 Gamma Function Appendix B. Linear Algebra Basics B.1 Positive Definite and Symmetric Matrices B.2 Correlation Matrix Diagonalization Appendix C. Cost Function Optimization C.1 Gradient Descent Algorithm C.2 Newton’s Algorithm C.3 Conjugate-Gradient Method C.4 Optimization for Constrained Problems Appendix D. Basic Definitions from Linear Systems Theory D.1 Linear Time Invariant (LTI) Systems D.2 Transfer Function D.3 Serial and Parallel Connection D.4 Two-Dimensional Generalizations

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