As with Volume I, this second volume represents a synthesis of issues in three historically distinct areas of learning research: computational learning theory, neural network research, and symbolic machine learning. While the first volume provided a forum for building a science of computational learning across fields, this volume attempts to define plausible areas of joint research: the contributions are concerned with finding constraints for theory while at the same time interpreting theoretic results in the context of experiments with actual learning systems. Subsequent volumes will focus on areas identified as research opportunities.
Computational learning theory, neural networks, and AI machine learning appear to be disparate fields; in fact they have the same goal: to build a machine or program that can learn from its environment. Accordingly, many of the papers in this volume deal with the problem of learning from examples. In particular, they are intended to encourage discussion between those trying to build learning algorithms (for instance, algorithms addressed by learning theoretic analyses are quite different from those used by neural network or machine-learning researchers) and those trying to analyze them.
The first section provides theoretical explanations for the learning systems addressed, the second section focuses on issues in model selection and inductive bias, the third section presents new learning algorithms, the fourth section explores the dynamics of learning in feedforward neural networks, and the final section focuses on the application of learning algorithms.
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Destinazione, tempi e costiDa: Kloof Booksellers & Scientia Verlag, Amsterdam, Paesi Bassi
Condizione: as new. Cambridge, MA: The MIT Press, 1994. Paperback. 584 pp.- As with Volume I, this second volume represents a synthesis of issues in three historically distinct areas of learning research: computational learning theory, neural network research, and symbolic machine learning. While the first volume provided a forum for building a science of computational learning across fields, this volume attempts to define plausible areas of joint research: the contributions are concerned with finding constraints for theory while at the same time interpreting theoretic results in the context of experiments with actual learning systems. Subsequent volumes will focus on areas identified as research opportunities. Computational learning theory, neural networks, and AI machine learning appear to be disparate fields; in fact they have the same goal: to build a machine or program that can learn from its environment. Accordingly, many of the papers in this volume deal with the problem of learning from examples. In particular, they are intended to encourage discussion between those trying to build learning algorithms (for instance, algorithms addressed by learning theoretic analyses are quite different from those used by neural network or machine-learning researchers) and those trying to analyze them. English text. Condition : as new. Condition : as new copy. ISBN 9780262581332. Keywords : , Codice articolo 250226
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Da: Wonder Book, Frederick, MD, U.S.A.
Condizione: Very Good. Very Good condition. Volume 2. A copy that may have a few cosmetic defects. May also contain a few markings such as an owner's name, short gifter's inscription or light stamp. Codice articolo K12O-00877
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Condizione: Sehr gut. Zustand: Sehr gut | Seiten: 584 | Sprache: Englisch | Produktart: Bücher. Codice articolo 41382081/202
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origi.Broschur, kl.4°, 449 Seiten. Einband bestossen sonst guter Zustand. Codice articolo 41656
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Da: PsychoBabel & Skoob Books, Didcot, Regno Unito
paperback. Condizione: Very Good. Condizione sovraccoperta: No Dust Jacket. Light shelfwear to cover edges but internally clean, tight and bright. Codice articolo 163778
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