Computational Learning Theory and Natural Learning Systems, Vol. III (MIT Press): Selecting Good Models: Selecting Good Models v. 3 (Bradford Books) - Softcover
Inhaltsangabe
This is the third in a series of edited volumes exploring the evolving landscape of learning systems research which spans theory and experiment, symbols and signals. It continues the exploration of the synthesis of the machine learning subdisciplines begun in volumes I and II. The nineteen contributions cover learning theory, empirical comparisons of learning algorithms, the use of prior knowledge, probabilistic concepts, and the effect of variations over time in the concepts and feedback from the environment. The goal of this series is to explore the intersection of three historically distinct areas of learning research: computational learning theory, neural networks and AI machine learning. Although each field has its own conferences, journals, language, research, results, and directions, there is a growing intersection and effort to bring these fields into closer coordination. Can the various communities learn anything from one another? These volumes present research that should be of interest to practitioners of the various subdisciplines of machine learning, addressing questions that are of interest across the range of machine learning approaches, comparing various approaches on specific problems and expanding the theory to cover more realistic cases. A Bradford Book
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Über die Autorin bzw. den Autor
Stephen Jose Hanson is Professor of Psychology (Newark Campus) and Member of the Cognitive Science Center (New Brunswick Campus) at Rutgers University.
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Computational Learning Theory and Natural Learning Systems, Volume III: Selecting Good Models
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Computational Learning Theory and Natural Learning Systems: Selecting Good Models, Volume 3
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Bradford Books, Cambridge, Massachusetts, U.S.A., 1995
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ISBN 13: 9780262660969
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Zustand: as new. Cambridge, MA: The MIT Press, 1995. Paperback. 405 pp.- This is the third in a series of edited volumes exploring the evolving landscape of learning systems research which spans theory and experiment, symbols and signals. It continues the exploration of the synthesis of the machine learning subdisciplines begun in volumes I and II. The nineteen contributions cover learning theory, empirical comparisons of learning algorithms, the use of prior knowledge, probabilistic concepts, and the effect of variations over time in the concepts and feedback from the environment. The goal of this series is to explore the intersection of three historically distinct areas of learning research: computational learning theory, neural networks andAI machine learning. Although each field has its own conferences, journals, language, research, results, and directions, there is a growing intersection and effort to bring these fields into closer coordination. English text. Condition : as new. Condition : as new copy. ISBN 9780262660969. Keywords : , Bestandsnummer des Verkäufers 250087
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