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.

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• Engineering & TransportationEngineeringComputer TechnologyImaging Systems
• Computers & TechnologyProgrammingPattern Recognition
• Engineering & TransportationEngineeringElectrical & ElectronicsElectricity Principles

• Publication Date: November 26, 2008
• Text-to-Speech: Disabled
• Lending: Disabled
• Print Length: 945 Pages
• File Size: 388 KB