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Hardcover. Condizione: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority.

hardcover. Condizione: Very Good. Very Good Condition - May show some limited signs of wear and may have a remainder mark. Pages and dust cover are intact and not marred by notes or highlighting.

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Condizione: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

Condizione: Fine. Used book that is in almost brand-new condition. May contain a remainder mark. Better World Books: Buy Books. Do Good.

Condizione: New.

Condizione: New.

Condizione: New.

Condizione: New.

Condizione: As New. Unread book in perfect condition.

Condizione: New.

Condizione: New.

Condizione: New.

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

Condizione: As New. Unread book in perfect condition.

Condizione: New.

Hardcover. Condizione: New. Brand New! Fast Delivery This is an International Edition and ship within 24-48 hours. Deliver by FedEx and Dhl, & Aramex, UPS, & USPS and we do accept APO and PO BOX Addresses. Order can be delivered worldwide within 6-10 days and we do have flat rate for up to 2LB. Extra shipping charges will be requested if the Book weight is more than 5 LB. This Item May be shipped from India, United states & United Kingdom. Depending on your location and availability. …

Condizione: New. 2024. paperback. . . . . .

Paperback. Condizione: New.

Condizione: As New. Unread book in perfect condition.

Condizione: New.

Condizione: 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. . . . .…

hardcover. Condizione: New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

Condizione: New. pp. 226.

Condizione: New. pp. 224.

Buch. Condizione: 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.…

Taschenbuch. Condizione: 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.…

Condizione: New. 2024. paperback. . . . . . Books ship from the US and Ireland.

Paperback. Condizione: Brand New. 214 pages. 10.00x8.00x10.00 inches. In Stock.

Condizione: 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.…