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.

zlibzh/no-category/（希）西奥多里德斯等著, (希)Sergios Theodoridis, (希)Konstantinos Koutroumbas著, Eodoridis Th, Utroumbas Ko/模式识别 英文版 第4版_115281903.pdf

Pattern recognition = Mo shi shi bie

Pattern Recognition Fourth Edition

Koutroumbas, Konstantinos, Theodoridis, Sergios

Konstantinos Koutroumbas; Sergios Theodoridis

Elsevier (Singapore) Pte Ltd

Elsevier/Academic Press

China Machine Press

Syngress Publishing

Elsevier Inc.

Jing dian yuan ban shu ku, 4th ed., English photoprint ed, Beijing, China, 2009

Jing dian yuan ban shu ku, Ying yin ban, Beijing, 2009

4th ed., Amsterdam, London, Massachusetts, 2009

United States, United States of America

4th ed, Burlington, MA ; London, ©2009

Elsevier Ltd., Burlington, MA, 2009

China, People's Republic, China

Fourth Edition, PT, 2008

Singapore, Singapore

4, 2008

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Previous ed.: Amsterdam: Academic, 2003.
Includes bibliographical references and index.

MiU

"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

2024-06-13