Computational learning theory and natural learning systems:
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 lear...
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| Weitere beteiligte Personen: | , , |
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| Format: | E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
©1994
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| Links: | https://doi.org/10.7551/mitpress/2007.001.0001?locatt=mode:legacy |
| Zusammenfassung: | 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. A Bradford Book. |
| Umfang: | 1 Online-Ressource |
| ISBN: | 026228684X 0262315831 0262581337 9780262286848 9780262315838 9780262581332 |
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| spelling | Computational learning theory and natural learning systems edited by Stephen J. Hanson, George A. Drastal, and Ronald L. Rivest ©1994 1 Online-Ressource txt c cr 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. A Bradford Book. Drastal, George A. Hanson, Stephen José Rivest, Ronald L. Erscheint auch als Druck-Ausgabe 0262581264 Erscheint auch als Druck-Ausgabe 0262660962 Erscheint auch als Druck-Ausgabe 9780262581264 Erscheint auch als Druck-Ausgabe 9780262660969 |
| spellingShingle | Computational learning theory and natural learning systems |
| title | Computational learning theory and natural learning systems |
| title_auth | Computational learning theory and natural learning systems |
| title_exact_search | Computational learning theory and natural learning systems |
| title_full | Computational learning theory and natural learning systems edited by Stephen J. Hanson, George A. Drastal, and Ronald L. Rivest |
| title_fullStr | Computational learning theory and natural learning systems edited by Stephen J. Hanson, George A. Drastal, and Ronald L. Rivest |
| title_full_unstemmed | Computational learning theory and natural learning systems edited by Stephen J. Hanson, George A. Drastal, and Ronald L. Rivest |
| title_short | Computational learning theory and natural learning systems |
| title_sort | computational learning theory and natural learning systems |
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