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245 10 a| Computational Learning Theory and Natural Learning Systemsh| [electronic resource]:b| Vol. 3: Selecting Good Models;

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520 8 a| Annotationb| <p>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.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</p>;

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Computational Learning Theory and Natural Learning Systems Vol. 3: Selecting Good Models

Author/creator	Petsche, Thomas Editor
Format	Electronic
Publication Info	Cambridge : MIT Press
Description	405 p. ill 09.000 x 07.000 in.
Supplemental Content	Full text available from MIT Press Direct to Open Backfile STEAM Monographs D2O
Subjects	Computational Learning Theory

Summary	Annotation <p>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.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</p>
Access restriction	Available only to authorized users.
Technical details	Mode of access: World Wide Web
Genre/form	Electronic books.
LCCN	93034468
ISBN	9780262660969
ISBN	0262660962 (Trade Paper) Active Record
Standard identifier#	9780262660969
Stock number	00015994

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