Pattern Recognition by S. Theodoridis

Book Description
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
1. Introduction
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

You can download this book from any of the following links. If any link is dead please feel free to leave a comment.
Download here (2nd Ed.)
Download here (3rd Ed.)
Download here (4th Ed.)

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