Pattern Recognition & Matlab Intro: Pattern Recognition

''This book is an excellent reference for pattern recognition, machine learning, and data mining. It focuses on the problems of classification and clustering, the two most important general problems in these areas. This book has tremendous breadth and depth in its coverage of these topics; it is clearly the best book available on the topic today. The new edition is an excellent up-to-date revision of the book. I have especially enjoyed the new coverage provided in several topics, including new viewpoints on Support Vector Machines, and the complete in-depth coverage of new clustering methods. This is a standout characteristic of this book: the coverage of the topics is solid, deep, and principled throughout. The book is very successful in bringing out the important points in each technique, while containing lots of interesting examples to explain complicated concepts. I believe the section on dimensionality reduction is an excellent exposition on this topic, among the best available, and this is just one example. Combined with a coverage unique in its extend, this makes the book appropriate for use as a reference, as a textbook for upper level undergraduate or graduate classes, and for the practitioner that wants to apply these techniques in practice. I am a professor in Computer Science. Although pattern recognition is not my main focus, I work in the related fields of data mining and databases. I have used this book for my own research and, very successfully, as teaching material. I would strongly recommend this book to both the academic student and the professional.''- Dimitrios Gunopoulos, University of California, Riverside, USA. ''I cut my pattern recognition teeth on a draft version of Duda and Hart (1973). Over subsequent decades, I consistently did two things: (i) recommended Duda and Hart as the best book available on pattern recognition; and (ii) wanted to write the next best book on this topic. I stopped (i) when the first edition ofS. Theodoridis andK. Koutroumbas'book appeared, and it supplanted the need for (ii) It was, and is, the best book that has been written on the subject since Duda and Hart's seminal original text. Buy it - you'll be happy you did.'' - Jim Bezdek, University of West Florida and Senior Fellow, U. of Melbourne (Australia). ''I consider the fourth edition of the book Pattern Recognition, by S. Theodoridis and K. Koutroumbas as the ''Bible of Pattern Recognition''- Simon Haykin, McMaster University, Canada ''I have taught a graduate course on statistical pattern recognition for more than twenty five years during which I have used many books with different levels of satisfaction. Recently, I adopted the book by Theodoridis and Koutroumbas (4 th edition) for my graduate course on statistical pattern recognition at University of Maryland. This course is taken by students from electrical engineering, computer science, linguistics and applied mathematics. The comprehensive book by Thedoridis and Koutroumbas covers both traditional and modern topics in statistical pattern recognition in a lucid manner, without compromising rigor. This book elegantly addresses the needs of graduate students from the different disciplines mentioned above. This is the only book that does justice to both supervised and unsupervised (clustering) techniques. Every student, researcher and instructor who is interested in any and all aspects of statistical pattern recognition will find this book extremely satisfying. I recommend it very highly.'' -Rama Chellappa, University of Maryland ''The book Pattern Recognition, by Profs. Sergios Theodoridis and Konstantinos Koutroumbas, has rapidly become the ''bible'' for teaching and learning the ins and outs of pattern recognition technology. In my own teaching, I have utilized the material in the first four chapters of the book (from basics to Bayes Decision Theory to Linear Classifiers and finally to Nonlinear Classifiers) in my class on fundamentals of speech recognition and have found the material to be presented in a clear and easily understandable manner, with excellent problems and ideas for projects. My students have all learned the basics of pattern recognition from this book and I highly recommend it to any serious student in this area.'' -Prof. Lawrence Rabiner

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).

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.

Content: Copyright , Page iv Preface , Pages xv-xvii Chapter 1 - Introduction , Pages 1-12 Chapter 2 - Classifiers Based on Bayes Decision Theory , Pages 13-89 Chapter 3 - Linear Classifiers , Pages 91-150 Chapter 4 - Nonlinear Classifiers , Pages 151-260 Chapter 5 - Feature Selection , Pages 261-322 Chapter 6 - Feature Generation I: Data Transformation and Dimensionality Reduction , Pages 323-409 Chapter 7 - Feature Generation II , Pages 411-479 Chapter 8 - Template Matching , Pages 481-519 Chapter 9 - Context-Dependent Classification , Pages 521-565 Chapter 10 - Supervised Learning: The Epilogue , Pages 567-594 Chapter 11 - Clustering: Basic Concepts , Pages 595-625 Chapter 12 - Clustering Algorithms I: Sequential Algorithms , Pages 627-652 Chapter 13 - Clustering Algorithms II: Hierarchical Algorithms , Pages 653-700 Chapter 14 - Clustering Algorithms III: Schemes Based on Function Optimization , Pages 701-763 Chapter 15 - Clustering Algorithms IV , Pages 765-862 Chapter 16 - Cluster Validity , Pages 863-913 Appendix A - Hints from Probability and Statistics , Pages 915-926 Appendix B - Linear Algebra Basics , Pages 927-929 Appendix C - Cost Function Optimization , Pages 930-945 Appendix D - Basic Definitions from Linear Systems Theory , Pages 946-948 Index , Pages 949-961 "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 have provided an up-to-date, self-contained volume encapsulating this wide spectrum of information. The very latest methods are incorporated in this edition including semi-supervised learning, non-linear dimensionality reduction techniques and spectral clustering."--Jacket Looks at the classical and the theory and practice, of both supervised and unsupervised 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 a self-contained volume encapsulating this wide spectrum of information. This updated volume 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