Introduction to Statistical Pattern Recognition

<br>Preface</br><br>Acknowledgments</br><br>Chapter 1 Introduction</br><br> 1.1 Formulation of Pattern Recognition Problems</br><br> 1.2 Process of Classifier Design</br><br> Notation</br><br> References</br><br>Chapter 2 Random Vectors and Their Properties</br><br> 2.1 Random Vectors and Their Distributions</br><br> 2.2 Estimation of Parameters</br><br> 2.3 Linear Transformation</br><br> 2.4 Various Properties of Eigenvalues and Eigenvectors</br><br> Computer Projects</br><br> Problems</br><br> References</br><br>Chapter 3 Hypothesis Testing</br><br> 3.1 Hypothesis Tests for Two Classes</br><br> 3.2 Other Hypothesis Tests</br><br> 3.3 Error Probability in Hypothesis Testing</br><br> 3.4 Upper Bounds on the Bayes Error</br><br> 3.5 Sequential Hypothesis Testing</br><br> Computer Projects</br><br> Problems</br><br> References</br><br>Chapter 4 Parametric Classifiers</br><br> 4.1 The Bayes Linear Classifier</br><br> 4.2 Linear Classifier Design</br><br> 4.3 Quadratic Classifier Design</br><br> 4.4 Other Classifiers</br><br> Computer Projects</br><br> Problems</br><br> References</br><br>Chapter5 Parameter Estimation</br><br> 5.1 Effect of Sample Size in Estimation</br><br> 5.2 Estimation of Classification Errors</br><br> 5.3 Holdout, Leave-One-Out, and Resubstitution Methods</br><br> 5.4 Bootstrap Methods</br><br> Computer Projects</br><br> Problems</br><br> References</br><br>Chapter 6 Nonparametric Density Estimation</br><br> 6.1 Parzen Density Estimate</br><br> 6.2 kNearest Neighbor Density Estimate</br><br> 6.3 Expansion by Basis Functions</br><br> Computer Projects</br><br> Problems</br><br> References</br><br>Chapter 7 Nonparametric Classification and Error Estimation</br><br> 7.1 General Discussion</br><br> 7.2 Voting kNN Procedure — Asymptotic Analysis</br><br> 7.3 Voting kNN Procedure — Finite Sample Analysis</br><br> 7.4 Error Estimation</br><br> 7.5 Miscellaneous Topics in the kNN Approach</br><br> Computer Projects</br><br> Problems</br><br> References</br><br>Chapter 8 Successive Parameter Estimation</br><br> 8.1 Successive Adjustment of a Linear Classifier</br><br> 8.2 Stochastic Approximation</br><br> 8.3 Successive Bayes Estimation</br><br> Computer Projects</br><br> Problems</br><br> References</br><br>Chapter 9 Feature Extraction and Linear Mapping for Signal Representation</br><br> 9.1 The Discrete Karhunen-Loéve Expansion</br><br> 9.2 The Karhunen-Loéve Expansion for Random Processes</br><br> 9.3 Estimation of Eigenvalues and Eigenvectors</br><br> Computer Projects</br><br> Problems</br><br> References</br><br>Chapter 10 Feature Extraction and Linear Mapping for Classification</br><br> 10.1 General Problem Formulation</br><br> 10.2 Discriminant Analysis</br><br> 10.3 Generalized Criteria</br><br> 10.4 Nonparametric Discriminant Analysis</br><br> 10.5 Sequential Selection of Quadratic Features</br><br> 10.6 Feature Subset Selection</br><br> Computer Projects</br><br> Problems</br><br> References</br><br>Chapter 11 Clustering</br><br> 11.1 Parametric Clustering</br><br> 11.2 Nonparametric Clustering</br><br> 11.3 Selection of Representatives</br><br> Computer Projects</br><br> Problems</br><br> References</br><br>Appendix A Derivatives of Matrices</br><br>Appendix B Mathematical Formulas</br><br>Appendix C Normal Error Table</br><br>Appendix D Gamma Function Table</br><br>Index</br>