9781402076473 - Machine Learning: Discriminative and Generative: 755 (The Springer International Series in Engineering and Computer Science, 755) de Jebara, Tony (18 resultados)

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hardcover. Condición: New. In shrink wrap. Looks like an interesting title!

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Condición: 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. . . . .

Condición: New. pp. 224.

Condición: 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.

Buch. 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.

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Hardcover. Condición: Like New. Like New. book.

Condición: As New. Unread book in perfect condition.

Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - 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. 224 pp. Englisch.

Gebunden. Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 boo.

Condición: New. Print on Demand pp. 224 Illus.

Condición: New. PRINT ON DEMAND pp. 224.

Buch. Condición: Neu. Machine Learning | Discriminative and Generative | Tony Jebara | Buch | xvii | Englisch | 2003 | Springer | EAN 9781402076473 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand.

Buch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Machine Learning is a powerful new field with many important practical applications. Thanks to the information age and flood of data, it has taken many domains by storm including biology, text processing, internet data organization, computer vision, speech recognition, computer-human interfaces, robotics, and artificial intelligence. This easy-access book covers the main contemporary themes and tools in machine learning, ranging from Bayesian probabilistic models to discriminative support-vector machines. Unlike previous books, it bridges these two schools of thought together within a common framework, elegantly connecting their various theories and combining their strengths into one common big-picture.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 224 pp. Englisch.