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ISBN 10: 0262039508 ISBN 13: 9780262039505
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Publicado por The MIT Press Bookstore, 2018
ISBN 10: 0262039508 ISBN 13: 9780262039505
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Publicado por The MIT Press Bookstore, 2018
ISBN 10: 0262039508 ISBN 13: 9780262039505
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Idioma: Inglés
Publicado por Kluwer Academic Publishers, 2003
ISBN 10: 1402076479 ISBN 13: 9781402076473
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Añadir al carritoCondición: New. Covers the main contemporary themes and tools in machine learning ranging from Bayesian probabilistic models to discriminative support-vector machines. This book offers the practical-minded engineer, student and the industrial public an easy-access road map into the world of machine learning. Series: The Springer International Series in Engineering and Computer Science. Num Pages: 200 pages, biography. BIC Classification: UM; UYQM. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly. Dimension: 234 x 156 x 14. Weight in Grams: 505. . 2003. Hardback. . . . .
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Añadir al carritoPaperback. Condición: Brand New. 214 pages. 10.00x8.00x10.00 inches. In Stock.
Idioma: Inglés
Publicado por Kluwer Academic Publishers, 2003
ISBN 10: 1402076479 ISBN 13: 9781402076473
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Añadir al carritoCondición: New. Covers the main contemporary themes and tools in machine learning ranging from Bayesian probabilistic models to discriminative support-vector machines. This book offers the practical-minded engineer, student and the industrial public an easy-access road map into the world of machine learning. Series: The Springer International Series in Engineering and Computer Science. Num Pages: 200 pages, biography. BIC Classification: UM; UYQM. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly. Dimension: 234 x 156 x 14. Weight in Grams: 505. . 2003. Hardback. . . . . Books ship from the US and Ireland.
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Añadir al carritoTaschenbuch. Condición: Neu. Machine Learning | Discriminative and Generative | Tony Jebara | Taschenbuch | xvii | Englisch | 2012 | Springer | EAN 9781461347569 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Idioma: Inglés
Publicado por Springer US, Springer US Dez 2003, 2003
ISBN 10: 1402076479 ISBN 13: 9781402076473
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Añadir al carritoBuch. Condición: Neu. Neuware -Machine Learning: Discriminative and Generative covers the main contemporary themes and tools in machine learning ranging from Bayesian probabilistic models to discriminative support-vector machines. However, unlike previous books that only discuss these rather different approaches in isolation, it bridges the two schools of thought together within a common framework, elegantly connecting their various theories and making one common big-picture. Also, this bridge brings forth new hybrid discriminative-generative tools that combine the strengths of both camps. This book serves multiple purposes as well. The framework acts as a scientific breakthrough, fusing the areas of generative and discriminative learning and will be of interest to many researchers. However, as a conceptual breakthrough, this common framework unifies many previously unrelated tools and techniques and makes them understandable to a larger portion of the public. This gives the more practical-minded engineer, student and the industrial public an easy-access and more sensible road map into the world of machine learning.Machine Learning: Discriminative and Generative is designed for an audience composed of researchers & practitioners in industry and academia. The book is also suitable as a secondary text for graduate-level students in computer science and engineering.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 224 pp. Englisch.
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Añadir al carritoTaschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - Machine Learning: Discriminative and Generative covers the main contemporary themes and tools in machine learning ranging from Bayesian probabilistic models to discriminative support-vector machines. However, unlike previous books that only discuss these rather different approaches in isolation, it bridges the two schools of thought together within a common framework, elegantly connecting their various theories and making one common big-picture. Also, this bridge brings forth new hybrid discriminative-generative tools that combine the strengths of both camps. This book serves multiple purposes as well. The framework acts as a scientific breakthrough, fusing the areas of generative and discriminative learning and will be of interest to many researchers. However, as a conceptual breakthrough, this common framework unifies many previously unrelated tools and techniques and makes them understandable to a larger portion of the public. This gives the more practical-minded engineer, student and the industrial public an easy-access and more sensible road map into the world of machine learning. Machine Learning: Discriminative and Generative is designed for an audience composed of researchers & practitioners in industry and academia. The book is also suitable as a secondary text for graduate-level students in computer science and engineering.
Idioma: Inglés
Publicado por Springer US, Springer US, 2003
ISBN 10: 1402076479 ISBN 13: 9781402076473
Librería: AHA-BUCH GmbH, Einbeck, Alemania
EUR 112,94
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Añadir al carritoBuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - Machine Learning: Discriminative and Generative covers the main contemporary themes and tools in machine learning ranging from Bayesian probabilistic models to discriminative support-vector machines. However, unlike previous books that only discuss these rather different approaches in isolation, it bridges the two schools of thought together within a common framework, elegantly connecting their various theories and making one common big-picture. Also, this bridge brings forth new hybrid discriminative-generative tools that combine the strengths of both camps. This book serves multiple purposes as well. The framework acts as a scientific breakthrough, fusing the areas of generative and discriminative learning and will be of interest to many researchers. However, as a conceptual breakthrough, this common framework unifies many previously unrelated tools and techniques and makes them understandable to a larger portion of the public. This gives the more practical-minded engineer, student and the industrial public an easy-access and more sensible road map into the world of machine learning. Machine Learning: Discriminative and Generative is designed for an audience composed of researchers & practitioners in industry and academia. The book is also suitable as a secondary text for graduate-level students in computer science and engineering.
Librería: Mispah books, Redhill, SURRE, Reino Unido
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Añadir al carritoHardcover. Condición: Like New. Like New. book.
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