商品簡介
This book considers classical and current theory and practice, of both supervised and unsupervised 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-date, self-contained volume encapsulating this wide spectrum of information. The very latest methods are incorporated in this edition: semi-supervised learning, combining clustering algorithms, and relevance feedback.This edition includes many more worked examples and diagrams (in two colour) to help give greater understanding of the methods and their application. Computer-based problems will be included with MATLAB code. An accompanying book contains extra worked examples and MATLAB code of all the examples used in this book.
目次
1. INTRODUCTION
2. CLASSIFIERS BASED ON BAYES DECISION THEORY
3. LINEAR CLASSIFIERS
4. NONLINEAR CLASSIFIERS
5. FEATURE SELECTION
6. FEATURE GENERATION I: DATA TRANSFORMATION AND DIMENSIONALITY REDUCTION
7. FEATURE GENERATION II
8. TEMPLATE MATCHING
9. CONTEXT DEPENDENT CLASSIFICATION
10. SYSTEM EVALUATION
11. CLUSTERING: BASIC CONCEPTS
12. CLUSTERING ALGORITHMS: ALGORITHMS L SEQUENTIAL
13. CLUSTERING ALGORITHMS II: HIERARCHICAL
14. CLUSTERING ALGORITHS III: BASED ON FUNCTION OPTIMIZATION
15. CLUSTERING ALGORITHMS IV: CLUSTERING
16. CLUSTER VALIDITY
