Statistical Pattern Recognition

Webb, Andrew R.

ISBN 10: 0470845147 ISBN 13: 9780470845141
Edité par Wiley & Sons, Incorporated, John, 2002
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Ships from the UK. Former library book; may include library markings. Used book that is in excellent condition. May show signs of wear or have minor defects. N° de réf. du vendeur GRP63649926

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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

Présentation de l'éditeur: 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.

For further information on the techniques and applications discussed in this book please visit www.statistical–pattern–recognition.net

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Détails bibliographiques

Titre : Statistical Pattern Recognition
Éditeur : Wiley & Sons, Incorporated, John
Date d'édition : 2002
Reliure : Couverture souple
Etat : Very Good
Edition : 2nd.

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Andrew R. Webb
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ISBN 10 : 0470845147 ISBN 13 : 9780470845141
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Webb, Andrew R.
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Kartoniert / Broschiert. Etat : 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. N° de réf. du vendeur 2298094259

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Taschenbuch. Etat : 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. N° de réf. du vendeur 9780470845141

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Taschenbuch. Etat : 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. N° de réf. du vendeur 9780470845141

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