Pattern Recognition
This book considers classical and current theory and practice, of supervised, unsupervised and semi-supervised pattern recognition, to build a complete background for professionals and students of engineering. The authors, leading experts in the field of pattern recognition, have provided an up-to-d...
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| Hlavní autoři: | , |
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| Médium: | E-kniha |
| Jazyk: | angličtina |
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Chantilly
Elsevier Science & Technology
2008
Academic Press |
| Vydání: | 4 |
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| ISBN: | 1597492728, 9781597492720 |
| On-line přístup: | Získat plný text |
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- 7.5.3 The Mel-Cepstrum -- 7.5.4 Spectral Features -- 7.5.5 Time Domain Features -- 7.5.6 An Example -- 7.6 Problems -- References -- Chapter 8. Template Matching -- 8.1 Introduction -- 8.2 Measures Based on Optimal Path Searching Techniques -- 8.2.1 Bellman's Optimality Principle and Dynamic Programming -- 8.2.2 The Edit Distance -- 8.2.3 Dynamic Time Warping in Speech Recognition -- 8.3 Measures Based on Correlations -- 8.4 Deformable Template Models -- 8.5 Content-Based Information Retrieval: Relevance Feedback -- 8.6 Problems -- 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 -- 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 -- 10.5.1 Generative Models -- 10.5.2 Graph-Based Methods -- 10.5.3 Transductive Support Vector Machines -- 10.6 Problems -- References -- Chapter 11. Clustering: Basic Concepts -- 11.1 Introduction -- 11.1.1 Applications of Cluster Analysis -- 11.1.2 Types of Features -- 11.1.3 Definitions of Clustering -- 11.2 Proximity Measures -- 11.2.1 Definitions -- 11.2.2 Proximity Measures between Two Points -- 11.2.3 Proximity Functions between a Point and a Set -- 11.2.4 Proximity Functions between Two Sets -- 11.3 Problems -- References -- Chapter 12. Clustering Algorithms I: Sequential Algorithms -- 12.1 Introduction -- 12.1.1 Number of Possible Clusterings -- 12.2 Categories of Clustering Algorithms
- 12.3 Sequential Clustering Algorithms -- 12.3.1 Estimation of the Number of Clusters -- 12.4 A Modification of BSAS -- 12.5 A Two-Threshold Sequential Scheme -- 12.6 Refinement Stages -- 12.7 Neural Network Implementation -- 12.7.1 Description of the Architecture -- 12.7.2 Implementation of the BSAS Algorithm -- 12.8 Problems -- References -- Chapter 13. Clustering Algorithms II: Hierarchical Algorithms -- 13.1 Introduction -- 13.2 Agglomerative Algorithms -- 13.2.1 Definition of Some Useful Quantities -- 13.2.2 Agglomerative Algorithms Based on Matrix Theory -- 13.2.3 Monotonicity and Crossover -- 13.2.4 Implementational Issues -- 13.2.5 Agglomerative Algorithms Based on Graph Theory -- 13.2.6 Ties in the Proximity Matrix -- 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 -- References -- Chapter 14. Clustering Algorithms III: Schemes Based on Function Optimization -- 14.1 Introduction -- 14.2 Mixture Decomposition Schemes -- 14.2.1 Compact and Hyperellipsoidal Clusters -- 14.2.2 A Geometrical Interpretation -- 14.3 Fuzzy Clustering Algorithms -- 14.3.1 Point Representatives -- 14.3.2 Quadric Surfaces as Representatives -- 14.3.3 Hyperplane Representatives -- 14.3.4 Combining Quadric and Hyperplane Representatives -- 14.3.5 A Geometrical Interpretation -- 14.3.6 Convergence Aspects of the Fuzzy Clustering Algorithms -- 14.3.7 Alternating Cluster Estimation -- 14.4 Possibilistic Clustering -- 14.4.1 The Mode-Seeking Property -- 14.4.2 An Alternative Possibilistic Scheme -- 14.5 Hard Clustering Algorithms -- 14.5.1 The Isodata or k-Means or c-Means Algorithm -- 14.5.2 k-Medoids Algorithms -- 14.6 Vector Quantization -- 14.7 Problems -- References -- Chapter 15. Clustering Algorithms IV -- 15.1 Introduction
- Front Cover -- Pattern Recognition -- Copyright Page -- Table of Contents -- Preface -- Acknowledgments -- 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 -- 2.4.1 The Gaussian Probability Density Function -- 2.4.2 The Bayesian Classifier for Normally Distributed Classes -- 2.5 Estimation of Unknown Probability Density Functions -- 2.5.1 Maximum Likelihood Parameter Estimation -- 2.5.2 Maximum a Posteriori Probability Estimation -- 2.5.3 Bayesian Inference -- 2.5.4 Maximum Entropy Estimation -- 2.5.5 Mixture Models -- 2.5.6 Nonparametric Estimation -- 2.5.7 The Naive-Bayes Classifier -- 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 -- 3.4.1 Mean Square Error Estimation -- 3.4.2 Stochastic Approximation and the LMS Algorithm -- 3.4.3 Sum of Error Squares Estimation -- 3.5 Mean Square Estimation Revisited -- 3.5.1 Mean Square Error Regression -- 3.5.2 MSE Estimates Posterior Class Probabilities -- 3.5.3 The Bias-Variance Dilemma -- 3.6 Logistic Discrimination -- 3.7 Support Vector Machines -- 3.7.1 Separable Classes -- 3.7.2 Nonseparable Classes -- 3.7.3 The Multiclass Case -- 3.7.4 ν-SVM -- 3.7.5 Support Vector Machines: A Geometric Viewpoint -- 3.7.6 Reduced Convex Hulls -- 3.8 Problems -- References -- Chapter 4. Nonlinear Classifiers
- 15.2 Clustering Algorithms Based on Graph Theory
- 5.7.1 Scalar Feature Selection -- 5.7.2 Feature Vector 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 -- 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 -- 6.5.1 ICA Based on Second- and Fourth-Order Cumulants -- 6.5.2 ICA Based on Mutual Information -- 6.5.3 An ICA Simulation Example -- 6.6 Nonnegative Matrix Factorization -- 6.7 Nonlinear Dimensionality Reduction -- 6.7.1 Kernel PCA -- 6.7.2 Graph-Based Methods -- 6.8 The Discrete Fourier Transform (DFT) -- 6.8.1 One-Dimensional DFT -- 6.8.2 Two-Dimensional 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 -- References -- Chapter 7. Feature Generation II -- 7.1 Introduction -- 7.2 Regional Features -- 7.2.1 Features for Texture Characterization -- 7.2.2 Local Linear Transforms for Texture Feature Extraction -- 7.2.3 Moments -- 7.2.4 Parametric Models -- 7.3 Features for Shape and Size Characterization -- 7.3.1 Fourier Features -- 7.3.2 Chain Codes -- 7.3.3 Moment-Based Features -- 7.3.4 Geometric Features -- 7.4 A Glimpse at Fractals -- 7.4.1 Self-Similarity and Fractal Dimension -- 7.4.2 Fractional Brownian Motion -- 7.5 Typical Features for Speech and Audio Classification -- 7.5.1 Short Time Processing of Signals -- 7.5.2 Cepstrum
- 4.1 Introduction -- 4.2 The XOR Problem -- 4.3 TheTwo-Layer Perceptron -- 4.3.1 Classification Capabilities of 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 -- 4.19.1 Expansion in Kernel Functions and Model Sparsification -- 4.19.2 Robust Statistics Regression -- 4.20 Decision Trees -- 4.20.1 Set of Questions -- 4.20.2 Splitting Criterion -- 4.20.3 Stop-Splitting Rule -- 4.20.4 Class Assignment Rule -- 4.21 Combining Classifiers -- 4.21.1 Geometric Average Rule -- 4.21.2 Arithmetic Average Rule -- 4.21.3 Majority Voting Rule -- 4.21.4 A Bayesian Viewpoint -- 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 -- 5.2.1 Outlier Removal -- 5.2.2 Data Normalization -- 5.2.3 Missing Data -- 5.3 The Peaking Phenomenon -- 5.4 Feature Selection Based on Statistical Hypothesis Testing -- 5.4.1 Hypothesis Testing Basics -- 5.4.2 Application of the t -Test in Feature Selection -- 5.5 The Receiver Operating Characteristics (ROC) Curve -- 5.6 Class Separability Measures -- 5.6.1 Divergence -- 5.6.2 Chernoff Bound and Bhattacharyya Distance -- 5.6.3 Scatter Matrices -- 5.7 Feature Subset Selection