COLT '89: Proceedings of the Second Annual Workshop, UC Santa Cruz, California, July 31 - August 2 1989
Computational Learning Theory presents the theoretical issues in machine learning and computational models of learning. This book covers a wide range of problems in concept learning, inductive inference, and pattern recognition.Organized into three parts encompassing 32 chapters, this book begins wi...
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| Format: | Elektronisch Tagungsbericht E-Book |
| Sprache: | English |
| Veröffentlicht: |
Saint Louis
Elsevier Science
2014
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| Online-Zugang: | FAW01 |
| Zusammenfassung: | Computational Learning Theory presents the theoretical issues in machine learning and computational models of learning. This book covers a wide range of problems in concept learning, inductive inference, and pattern recognition.Organized into three parts encompassing 32 chapters, this book begins with an overview of the inductive principle based on weak convergence of probability measures. This text then examines the framework for constructing learning algorithms. Other chapters consider the formal theory of learning, which is learning in the sense of improving computational efficiency as opposed to concept learning. This book discusses as well the informed parsimonious (IP) inference that generalizes the compatibility and weighted parsimony techniques, which are most commonly applied in biology. The final chapter deals with the construction of prediction algorithms in a situation in which a learner faces a sequence of trials, with a prediction to be given in each and the goal of the learner is to make some mistakes.This book is a valuable resource for students and teachers |
| Beschreibung: | Description based on publisher supplied metadata and other sources |
| Beschreibung: | 1 online resource (397 pages) |
| ISBN: | 9780080948294 9781558600867 |
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| 520 | |a Computational Learning Theory presents the theoretical issues in machine learning and computational models of learning. This book covers a wide range of problems in concept learning, inductive inference, and pattern recognition.Organized into three parts encompassing 32 chapters, this book begins with an overview of the inductive principle based on weak convergence of probability measures. This text then examines the framework for constructing learning algorithms. Other chapters consider the formal theory of learning, which is learning in the sense of improving computational efficiency as opposed to concept learning. This book discusses as well the informed parsimonious (IP) inference that generalizes the compatibility and weighted parsimony techniques, which are most commonly applied in biology. The final chapter deals with the construction of prediction algorithms in a situation in which a learner faces a sequence of trials, with a prediction to be given in each and the goal of the learner is to make some mistakes.This book is a valuable resource for students and teachers | ||
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Datensatz im Suchindex
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| dewey-ones | 006 - Special computer methods |
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| dewey-sort | 16.3 11 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| format | Electronic Conference Proceeding eBook |
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| indexdate | 2024-07-10T07:30:54Z |
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| isbn | 9780080948294 9781558600867 |
| language | English |
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| spelling | Warmuth, Manfred K. Verfasser aut COLT '89 Proceedings of the Second Annual Workshop, UC Santa Cruz, California, July 31 - August 2 1989 Saint Louis Elsevier Science 2014 © 1989 1 online resource (397 pages) txt rdacontent c rdamedia cr rdacarrier Description based on publisher supplied metadata and other sources Computational Learning Theory presents the theoretical issues in machine learning and computational models of learning. This book covers a wide range of problems in concept learning, inductive inference, and pattern recognition.Organized into three parts encompassing 32 chapters, this book begins with an overview of the inductive principle based on weak convergence of probability measures. This text then examines the framework for constructing learning algorithms. Other chapters consider the formal theory of learning, which is learning in the sense of improving computational efficiency as opposed to concept learning. This book discusses as well the informed parsimonious (IP) inference that generalizes the compatibility and weighted parsimony techniques, which are most commonly applied in biology. The final chapter deals with the construction of prediction algorithms in a situation in which a learner faces a sequence of trials, with a prediction to be given in each and the goal of the learner is to make some mistakes.This book is a valuable resource for students and teachers Computational learning theory -- Congresses (DE-588)1071861417 Konferenzschrift gnd-content Workshop on Computational Learning Theory 2 1989 Santa Cruz, Calif. Sonstige (DE-588)5030223-1 oth Erscheint auch als Druck-Ausgabe Warmuth, Manfred K . COLT '89 : Proceedings of the Second Annual Workshop, UC Santa Cruz, California, July 31 - August 2 1989 |
| spellingShingle | Warmuth, Manfred K. COLT '89 Proceedings of the Second Annual Workshop, UC Santa Cruz, California, July 31 - August 2 1989 Computational learning theory -- Congresses |
| subject_GND | (DE-588)1071861417 |
| title | COLT '89 Proceedings of the Second Annual Workshop, UC Santa Cruz, California, July 31 - August 2 1989 |
| title_auth | COLT '89 Proceedings of the Second Annual Workshop, UC Santa Cruz, California, July 31 - August 2 1989 |
| title_exact_search | COLT '89 Proceedings of the Second Annual Workshop, UC Santa Cruz, California, July 31 - August 2 1989 |
| title_full | COLT '89 Proceedings of the Second Annual Workshop, UC Santa Cruz, California, July 31 - August 2 1989 |
| title_fullStr | COLT '89 Proceedings of the Second Annual Workshop, UC Santa Cruz, California, July 31 - August 2 1989 |
| title_full_unstemmed | COLT '89 Proceedings of the Second Annual Workshop, UC Santa Cruz, California, July 31 - August 2 1989 |
| title_short | COLT '89 |
| title_sort | colt 89 proceedings of the second annual workshop uc santa cruz california july 31 august 2 1989 |
| title_sub | Proceedings of the Second Annual Workshop, UC Santa Cruz, California, July 31 - August 2 1989 |
| topic | Computational learning theory -- Congresses |
| topic_facet | Computational learning theory -- Congresses Konferenzschrift |
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