Title:
Introduction to statistical pattern recognition
Personal Author:
Edition:
2nd ed.
Publication Information:
Boston : Academic Press, 1990
ISBN:
9780122698514
Available:*
Library | Item Barcode | Call Number | Material Type | Item Category 1 | Status |
|---|---|---|---|---|---|
Searching... | 30000000887319 | Q327 K44 1990 | Open Access Book | Book | Searching... |
On Order
Summary
Summary
This completely revised second edition presents an introduction to statistical pattern recognition. Pattern recognition in general covers a wide range of problems: it is applied to engineering problems, such as character readers and wave form analysis as well as to brain modeling in biology and psychology. Statistical decision and estimation, which are the main subjects of this book, are regarded as fundamental to the study of pattern recognition. This book is appropriate as a text for introductory courses in pattern recognition and as a reference book for workers in the field. Each chapter contains computer projects as well as exercises.
Table of Contents
| Preface |
| Acknowledgments |
| Chapter 1 Introduction |
| 1.1 Formulation of Pattern Recognition Problems |
| 1.2 Process of Classifier Design |
| Notation |
| References |
| Chapter 2 Random Vectors and Their Properties |
| 2.1 Random Vectors and Their Distributions |
| 2.2 Estimation of Parameters |
| 2.3 Linear Transformation |
| 2.4 Various Properties of Eigenvalues and Eigenvectors |
| Computer Projects |
| Problems |
| References |
| Chapter 3 Hypothesis Testing |
| 3.1 Hypothesis Tests for Two Classes |
| 3.2 Other Hypothesis Tests |
| 3.3 Error Probability in Hypothesis Testing |
| 3.4 Upper Bounds on the Bayes Error |
| 3.5 Sequential Hypothesis Testing |
| Computer Projects |
| Problems |
| References |
| Chapter 4 Parametric Classifiers |
| 4.1 The Bayes Linear Classifier |
| 4.2 Linear Classifier Design |
| 4.3 Quadratic Classifier Design |
| 4.4 Other Classifiers |
| Computer Projects |
| Problems |
| References |
| Chapter 5 Parameter Estimation |
| 5.1 Effect of Sample Size in Estimation |
| 5.2 Estimation of Classification Errors |
| 5.3 Holdout, Leave-One-Out, and Resubstitution Methods |
| 5.4 Bootstrap Methods |
| Computer Projects |
| Problems |
| References |
| Chapter 6 Nonparametric Density Estimation |
| 6.1 Parzen Density Estimate |
| 6.2 kNearest Neighbor Density Estimate |
| 6.3 Expansion by Basis Functions |
| Computer Projects |
| Problems |
| References |
| Chapter 7 Nonparametric Classification and Error Estimation |
| 7.1 General Discussion |
| 7.2 Voting kNN Procedure - Asymptotic Analysis |
| 7.3 Voting kNN Procedure - Finite Sample Analysis |
| 7.4 Error Estimation |
| 7.5 Miscellaneous Topics in the kNN Approach |
| Computer Projects |
| Problems |
| References |
| Chapter 8 Successive Parameter Estimation |
| 8.1 Successive Adjustment of a Linear Classifier |
| 8.2 Stochastic Approximation |
| 8.3 Successive Bayes Estimation |
| Computer Projects |
| Problems |
| References |
| Chapter 9 Feature Extraction and Linear Mapping for Signal Representation |
| 9.1 The Discrete Karhunen-Loéve Expansion |
| 9.2 The Karhunen-Loéve Expansion for Random Processes |
| 9.3 Estimation of Eigenvalues and Eigenvectors |
| Computer Projects |
| Problems |
| References |
| Chapter 10 Feature Extraction and Linear Mapping for Classification |
| 10.1 General Problem Formulation |
| 10.2 Discriminant Analysis |
| 10.3 Generalized Criteria |
| 10.4 Nonparametric Discriminant Analysis |
| 10.5 Sequential Selection of Quadratic Features |
| 10.6 Feature Subset Selection |
| Computer Projects |
| Problems |
| References |
| Chapter 11 Clustering |
| 11.1 Parametric Clustering |
| 11.2 Nonparametric Clustering |
| 11.3 Selection of Representatives |
| Computer Projects |
| Problems |
| References |
| Appendix A Derivatives of Matrices |
| Appendix B Mathematical Formulas |
| Appendix C Normal Error Table |
| Appendix D Gamma Function Table |
| Index |
