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-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.
Table of Contents
2. Classifiers based on Bayes Decision
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 Depedant Clarification
10. System Evaultion
11. Clustering: Basic Concepts
12. Clustering Algorithms: Algorithms L Sequential
13. Clustering Algorithms II: Hierarchical
14. Clustering Algorithms III: Based on Function Optimization
15. Clustering Algorithms IV: Clustering
16. Cluster Validity
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