Isbn: 9780262530880 - machine learning: paradigms and methods (18 risultati)

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Condizione: Very Good. 0th Edition. Former library copy. Pages intact with possible writing/highlighting. Binding strong with minor wear. Dust jackets/supplements may not be included. Includes library markings. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.…

Paperback. Reprint edition. Very Good/Wraps (17535) A very good trade paperback with lightly rubbed wraps. Clean, tight, unmarked. .

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Condizione: New.

Paperback or Softback. Condizione: New. Machine Learning: Paradigms and Methods. Book.

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Condizione: New.

Condizione: As New. Unread book in perfect condition.

Paperback. Condizione: Brand New. 400 pages. 9.00x6.25x1.00 inches. In Stock.

Condizione: New. Print on Demand pp. 400.

Condizione: New. Print on Demand pp. 400.

Condizione: New. PRINT ON DEMAND pp. 400.

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. KlappentextHaving played a central role at the inception of artificial intelligence research, machine learning has recently reemerged as a major area of study at the very core of the subject.…

Taschenbuch. Condizione: Neu. Machine Learning | Paradigms and Methods | Jaime Carbonell | Taschenbuch | Kartoniert / Broschiert | Englisch | 1990 | MIT Press | EAN 9780262530880 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand. …

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Having played a central role at the inception of artificial intelligence research, machine learning has recently reemerged as a major area of study at the very core of the subject. Solid theoretical foundations are being constructed. Machine learning methods are being integrated with powerful performance systems, and practical applications; based on established techniques are emerging.Machine Learning unifies the field by bringing together and clearly explaining the major successful paradigms for machine learning: inductive approaches, explanation-based learning, genetic algorithms, and connectionist learning methods. Each paradigm is presented in depth, providing historical perspective but focusing on current research and potential applications.ContributorsJohn R. Anderson, L. B. Booker, John. H. Gennari, Jaime G. Carbonell, Oren Etzioni, Doug Fisher, Yolanda Gil, D. E. Goldberg, Gerald E. Hinton, J. H. Holland, Craig A Knoblock, Daniel. R. Kuokka, Pat Langley, David B. Leake, Steve Minton, Jack Mostow, Roger C. Schank, and Jan M. Zytkow.…