Statistical Pattern Recognition

Webb, Andrew R.

ISBN 10: 0470845147 ISBN 13: 9780470845141
Published by Wiley & Sons, Incorporated, John, 2002
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Statistical pattern recognition is a very active area of study and research, which has seen many advances in recent years. New and emerging applications -- such as data mining, web searching, multimedia data retrieval, face recognition, and cursive handwriting recognition -- require robust and efficient pattern recognition techniques. Statistical decision making and estimation are regarded as fundamental to the study of pattern recognition. Statistical Pattern Recognition, Second Edition has been fully updated with new methods, applications and references. It provides a comprehensive introduction to this vibrant area -- with material drawn from engineering, statistics, computer science and the social sciences -- and covers many application areas, such as database design, artificial neural networks, and decision support systems. aeo Provides a self--contained introduction to statistical pattern recognition. aeo Each technique described is illustrated by real examples. aeo Covers Bayesian methods, neural networks, support vector machines, and unsupervised classification. aeo Each section concludes with a description of the applications that have been addressed and with further developments of the theory. aeo Includes background material on dissimilarity, parameter estimation, data, linear algebra and probability. aeo Features a variety of exercises, from a open--booka questions to more lengthy projects. The book is aimed primarily at senior undergraduate and graduate students studying statistical pattern recognition, pattern processing, neural networks, and data mining, in both statistics and engineering departments. It is also an excellent source of reference for technical professionals working in advanced information development environments. For further information on the techniques and applications discussed in this book please visit www.statistical--pattern--recognition.net

From the Back Cover: Statistical pattern recognition is a very active area of study and research, which has seen many advances in recent years. New and emerging applications - such as data mining, web searching, multimedia data retrieval, face recognition, and cursive handwriting recognition - require robust and efficient pattern recognition techniques. Statistical decision making and estimation are regarded as fundamental to the study of pattern recognition.

Statistical Pattern Recognition, Second Edition has been fully updated with new methods, applications and references. It provides a comprehensive introduction to this vibrant area - with material drawn from engineering, statistics, computer science and the social sciences - and covers many application areas, such as database design, artificial neural networks, and decision support systems.
* Provides a self-contained introduction to statistical pattern recognition.

* Each technique described is illustrated by real examples.

* Covers Bayesian methods, neural networks, support vector machines, and unsupervised classification.

* Each section concludes with a description of the applications that have been addressed and with further developments of the theory.

* Includes background material on dissimilarity, parameter estimation, data, linear algebra and probability.

* Features a variety of exercises, from 'open-book' questions to more lengthy projects.
The book is aimed primarily at senior undergraduate and graduate students studying statistical pattern recognition, pattern processing, neural networks, and data mining, in both statistics and engineering departments. It is also an excellent source of reference for technical professionals working in advanced information development environments.

"About this title" may belong to another edition of this title.

Bibliographic Details

Title: Statistical Pattern Recognition
Publisher: Wiley & Sons, Incorporated, John
Publication Date: 2002
Binding: Soft cover
Condition: Very Good
Edition: 2nd.

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Andrew R. Webb
Published by Wiley, Estados Unidos, 2003
ISBN 10: 0470845147 ISBN 13: 9780470845141
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Webb, Andrew R.
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Published by John Wiley & Sons 07.2002., 2002
ISBN 10: 0470845147 ISBN 13: 9780470845141
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paperback. Condition: Sehr gut. Auflage: 2. Auflage. minimale Lagerspuren am Buch, Inhalt einwandfrei und ungelesen 212709 Sprache: Englisch Gewicht in Gramm: 915. Seller Inventory # 219242

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Condition: Good. Your purchase helps support Sri Lankan Children's Charity 'The Rainbow Centre'. Ex-library, so some stamps and wear, but in good overall condition. Our donations to The Rainbow Centre have helped provide an education and a safe haven to hundreds of children who live in appalling conditions. Seller Inventory # Z1-C-088-07240

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Kartoniert / Broschiert. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This book provides an introduction to statistical pattern theory and techniques, with material drawn from a wide range of fields, including the areas of engineering, statistics, computer science and the social sciences. It provides a valuable link between t. Seller Inventory # 2298094259

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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Statistical pattern recognition is a very active area of study and research, which has seen many advances in recent years. New and emerging applications - such as data mining, web searching, multimedia data retrieval, face recognition, and cursive handwriting recognition - require robust and efficient pattern recognition techniques. Statistical decision making and estimation are regarded as fundamental to the study of pattern recognition.Statistical Pattern Recognition, Second Edition has been fully updated with new methods, applications and references. It provides a comprehensive introduction to this vibrant area - with material drawn from engineering, statistics, computer science and the social sciences - and covers many application areas, such as database design, artificial neural networks, and decision support systems. Provides a self-contained introduction to statistical pattern recognition. Each technique described is illustrated by real examples. Covers Bayesian methods, neural networks, support vector machines, and unsupervised classification. Each section concludes with a description of the applications that have been addressed and with further developments of the theory. Includes background material on dissimilarity, parameter estimation, data, linear algebra and probability. Features a variety of exercises, from 'open-book' questions to more lengthy projects. The book is aimed primarily at senior undergraduate and graduate students studying statistical pattern recognition, pattern processing, neural networks, and data mining, in both statistics and engineering departments. It is also an excellent source of reference for technical professionals working in advanced information development environments. Statistical pattern recognition is a very active area of study and research, which has seen many advances in recent years. New and emerging applications - such as data mining, web searching, multimedia data retrieval, face recognition, and cursive handwriting recognition - require robust and efficient pattern recognition techniques. Statistical decision making and estimation are regarded as fundamental to the study of pattern recognition. Statistical Pattern Recognition, Second Edition has been fully updated with new methods, applications and references. It provides a comprehensive introduction to this vibrant area - with material drawn from engineering, statistics, computer science and the social sciences - and covers many application areas, such as database design, artificial neural networks, and decision support systems. Provides a self-contained introduction to statistical pattern recognition. Each technique described is illustrated by real examples. Covers Bayesian methods, neural networks, support vector machines, and unsupervised classification. Each section concludes with a description of the applications that have been addressed and with further developments of the theory. Includes background material on dissimilarity, parameter estimation, data, linear algebra and probability. Features a variety of exercises, from 'open-book' questions to more lengthy projects. The book is aimed primarily at senior undergraduate and graduate students studying statistical pattern recognition, pattern processing, neural networks, and data mining, in both statistics and engineering departments. It is also an excellent source of reference for technical professionals working in advanced information development environments. 448 pp. Englisch. Seller Inventory # 9780470845141

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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Statistical pattern recognition is a very active area of study and research, which has seen many advances in recent years. New and emerging applications - such as data mining, web searching, multimedia data retrieval, face recognition, and cursive handwriting recognition - require robust and efficient pattern recognition techniques. Statistical decision making and estimation are regarded as fundamental to the study of pattern recognition.Statistical Pattern Recognition, Second Edition has been fully updated with new methods, applications and references. It provides a comprehensive introduction to this vibrant area - with material drawn from engineering, statistics, computer science and the social sciences - and covers many application areas, such as database design, artificial neural networks, and decision support systems. Provides a self-contained introduction to statistical pattern recognition. Each technique described is illustrated by real examples. Covers Bayesian methods, neural networks, support vector machines, and unsupervised classification. Each section concludes with a description of the applications that have been addressed and with further developments of the theory. Includes background material on dissimilarity, parameter estimation, data, linear algebra and probability. Features a variety of exercises, from 'open-book' questions to more lengthy projects. The book is aimed primarily at senior undergraduate and graduate students studying statistical pattern recognition, pattern processing, neural networks, and data mining, in both statistics and engineering departments. It is also an excellent source of reference for technical professionals working in advanced information development environments. Statistical pattern recognition is a very active area of study and research, which has seen many advances in recent years. New and emerging applications - such as data mining, web searching, multimedia data retrieval, face recognition, and cursive handwriting recognition - require robust and efficient pattern recognition techniques. Statistical decision making and estimation are regarded as fundamental to the study of pattern recognition. Statistical Pattern Recognition, Second Edition has been fully updated with new methods, applications and references. It provides a comprehensive introduction to this vibrant area - with material drawn from engineering, statistics, computer science and the social sciences - and covers many application areas, such as database design, artificial neural networks, and decision support systems. Provides a self-contained introduction to statistical pattern recognition. Each technique described is illustrated by real examples. Covers Bayesian methods, neural networks, support vector machines, and unsupervised classification. Each section concludes with a description of the applications that have been addressed and with further developments of the theory. Includes background material on dissimilarity, parameter estimation, data, linear algebra and probability. Features a variety of exercises, from 'open-book' questions to more lengthy projects. The book is aimed primarily at senior undergraduate and graduate students studying statistical pattern recognition, pattern processing, neural networks, and data mining, in both statistics and engineering departments. It is also an excellent source of reference for technical professionals working in advanced information development environments. Seller Inventory # 9780470845141

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