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基本説明
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. This edition includes many more worked examples and diagrams to help give greater understanding of the methods and their application. Computer-based problems will be included with Matlab code. An accompanying book Matlab introduction to Pattern Recognition (Sep 2008, ISBN-10/13: 978-0-12-374486-9).
Full 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.
· 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.
· More Matlab code is available, together with an accompanying manual, via this site
· 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.
· An accompanying book with Matlab code of the most common methods and algorithms in the book, together with a descriptive summary, and solved examples including real-life data sets in imaging, and audio recognition. The companion book will be available separately or at a special packaged price (ISBN: 9780123744869).
Contents
1. Introduction2. Classifiers based on Bayes Decision 3. Linear Classifiers4. Nonlinear Classifiers5. Feature Selection6. Feature Generation I: Data Transformation and Dimensionality Reduction7. Feature Generation II8. Template Matching 9. Context Depedant Clarification10. System Evaultion11. Clustering: Basic Concepts12. Clustering Algorithms: Algorithms L Sequential 13. Clustering Algorithms II: Hierarchical 14. Clustering Algorithms III: Based on Function Optimization 15. Clustering Algorithms IV: Clustering16. Cluster Validity
