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).
What people are saying - Write a review
The book that is actually provided when this book is purchased is 'Plastic Analysis and Design of Steel Structures' by M. Bill Wong
this is a very very good book for pattern recognition. describe all concepts from base level.
but drawback is high price, no low price edition.- SureshD, JRF,ISM
Chapter 4 Nonlinear Classifiers
Chapter 5 Feature Selection
Data Transformation and Dimensionality Reduction
Chapter 7 Feature Generation II
Chapter 8 Template Matching
Schemes Based on Function Optimization
Chapter 15 Clustering Algorithms IV
Chapter 16 Cluster Validity
Appendix A Hints from Probabilityand Statistics
Appendix B Linear Algebra Basics
Appendix C Cost Function Optimization