Instruction to Statistical Pattern Recognition

PrefaceAcknowledgmentsChapter 1 Introduction 1.1 Formulation of Pattern Recognition Problems 1.2 Chapter OutlinesChapter 2 Random Vectors and Their Properties 2.1 Random Vectors and Their Distributions 2.2 Properties of Distributions 2.3 Transformation of Random Vectors 2.4 Various Properties of Eigenvalues and Eigenvectors Standard Data Computer Projects ProblemsChapter 3 Hypothesis Testing 3.1 Simple Hypothesis Tests 3.2 Error Probability in Hypothesis Testing 3.3 Upper Bounds on Error Probability 3.4 Other Hypothesis Tests 3.5 Sequential Hypothesis Testing Computer Projects ProblemsChapter 4 Linear Classifiers 4.1 The Bayes Linear Classifier 4.2 Linear Discriminant Function for Minimum Error 4.3 Linear Discriminant Function for Minimum Mean-Square Error 4.4 Desired Output and Mean-Square Error 4.5 Other Discriminant Functions Computer Projects ProblemsChapter 5 Parameter Estimation 5.1 Estimation of Nonrandom Parameters 5.2 Estimation of Random Parameters 5.3 Interval Estimation 5.4 Estimation of the Probability of Error Appendix 5-1 Calculation of the Bias between the C Method and the Leaving-One-Out Method Computer Projects ProblemsChapter 6 Estimation of Density Functions 6.1 Parzen Estimate 6.2 k-Nearest Neighbor Approach 6.3 Histogram Approach 6.4 Expansion by Basis Functions Computer Projects ProblemsChapter 7 Successive Parameter Estimation 7.1 Successive Adjustment of a Linear Classifier 7.2 Stochastic Approximation 7.3 Successive Bayes Estimation Computer Projects ProblemsChapter 8 Feature Selection and Linear Mapping for One Distribution 8.1 The Discrete Karhunen-Loève Expansion 8.2 Other Criteria for One Distribution 8.3 The Karhunen-Loève Expansion for Random Processes 8.4 Estimation of Eigenvalues and Eigenvectors APPENDIX 8-1 Calculation of E{(FiTSFj)2} APPENDIX 8-2 Rapid Eigenvalue-Eigenvector Calculation Computer Projects ProblemsChapter 9 Feature Selection and Linear Mapping for Multidistributions 9.1 General Properties of Class Separability 9.2 Discriminant Analysis 9.3 The Chernoff Bound and the Bhattacharyya Distance 9.4 Divergence Computer Projects ProblemsChapter 10 Nonlinear Mapping 10.1 Intrinsic Dimensionality of Data 10.2 Separability Enhancement by Nonlinear Mapping 10.3 Two-Dimensional Displays Computer ProjectsChapter 11 Clustering 11.1 An Algorithm for Clustering 11.2 Parametric Clustering Criteria 11.3 Nonparametric Clustering Criteria 11.4 Additional Clustering Procedures Computer ProjectsReferencesIndex