9780470845141 - statistical pattern recognition de webb, andrew r. (8 resultados)
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Librería: ThriftBooks-Dallas, Dallas, TX, Estados Unidos de AmericaThriftBooks-Dallas
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EUR 36,44
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Paperback. Condición: Very Good. No Jacket. May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less.
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Librería: Phatpocket Limited, Waltham Abbey, HERTS, Reino UnidoPhatpocket Limited
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EUR 41,88
Envío por EUR 12,44Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Condición: 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.
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Librería: Revaluation Books, Exeter, Reino UnidoRevaluation Books
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EUR 60,58
Envío por EUR 14,62Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Paperback. Condición: Brand New. 2nd edition. 534 pages. 9.50x6.75x1.50 inches. In Stock.
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Librería: Buchpark, Trebbin, AlemaniaBuchpark
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EUR 21,67
Envío por EUR 105,00Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Condición: Gut. Zustand: Gut | Seiten: 514 | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar.
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Librería: Vulkaneifel Bücher, Birgel, AlemaniaVulkaneifel Bücher
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EUR 19,95
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paperback. Condición: Sehr gut. Auflage: 2. Auflage. minimale Lagerspuren am Buch, Inhalt einwandfrei und ungelesen Sprache: Englisch Gewicht in Gramm: 915.
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Librería: The Book Spot, Sioux Falls, MN, Estados Unidos de AmericaThe Book Spot
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EUR 267,09
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Paperback. Condición: New.
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, AlemaniaBuchWeltWeit Ludwig Meier e.K.
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EUR 57,90
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Taschenbuch. Condición: 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 recogniti…on, 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.
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Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 62,93
Envío por EUR 64,66Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 2 disponibles
Taschenbuch. Condición: 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, a…nd 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.
