[FOX-Ebook]Pattern Recognition, 4th Edition

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.

• Thoroughly developed to include many more worked examples to give greater understanding of the various methods and techniques
• Many more diagrams included–now in two color–to provide greater insight through visual presentation
• Matlab code of the most common methods are given at the end of each chapter
• An accompanying book with Matlab code of the most common methods and algorithms in the book, together with a descriptive summary and solved examples, and including real-life data sets in imaging and audio recognition. The companion book is available separately or at a special packaged price (Book ISBN: 9780123744869. Package ISBN: 9780123744913)
• Latest hot topics included to further the reference value of the text including non-linear dimensionality reduction techniques, relevance feedback, semi-supervised learning, spectral clustering, combining clustering algorithms
• Solutions manual, powerpoint slides, and additional resources are available to faculty using the text for their course. Register at www.textbooks.elsevier.com and search on “Theodoridis” to access resources for instructor.

Table of Contents

Charpter 1. Introduction
Charpter 2. Classifiers Based On Bayes Decision Theory
Charpter 3. Linear Classifiers
Charpter 4. Nonlinear Classifiers
Charpter 5. Feature Selection
Charpter 6. Feature Generation I: Data Transformation And Dimensionality Reduction
Charpter 7. Feature Generation II
Charpter 8. Template Matching
Charpter 9. Context-Dependent Classification
Charpter 10. Supervised Learning: The Epilogue
Charpter 11. Clustering: Basic Concepts
Charpter 12. Clustering Algorithms I: Sequential Algorithms
Charpter 13. Clustering Algorithms II: Hierarchical Algorithms
Charpter 14. Clustering Algorithms III: Schemes Based On Function Optimization
Charpter 15. Clustering Algorithms IV
Charpter 16. Cluster Validity