Extending Explanation-Based Learning by Generalizing the Structure of Explanations (PDF)
(Sprache: Englisch)
Extending Explanation-Based Learning by Generalizing the Structure of Explanations presents several fully-implemented computer systems that reflect theories of how to extend an interesting subfield of machine learning called explanation-based...
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Extending Explanation-Based Learning by Generalizing the Structure of Explanations presents several fully-implemented computer systems that reflect theories of how to extend an interesting subfield of machine learning called explanation-based learning.
This book discusses the need for generalizing explanation structures, relevance to research areas outside machine learning, and schema-based problem solving. The result of standard explanation-based learning, BAGGER generalization algorithm, and empirical analysis of explanation-based learning are also elaborated. This text likewise covers the effect of increased problem complexity, rule access strategies, empirical study of BAGGER2, and related work in similarity-based learning.
This publication is suitable for readers interested in machine learning, especially explanation-based learning.
This book discusses the need for generalizing explanation structures, relevance to research areas outside machine learning, and schema-based problem solving. The result of standard explanation-based learning, BAGGER generalization algorithm, and empirical analysis of explanation-based learning are also elaborated. This text likewise covers the effect of increased problem complexity, rule access strategies, empirical study of BAGGER2, and related work in similarity-based learning.
This publication is suitable for readers interested in machine learning, especially explanation-based learning.
Bibliographische Angaben
- Autor: Jude W. Shavlik
- 2014, 236 Seiten, Englisch
- Verlag: Elsevier Science & Techn.
- ISBN-10: 1483258912
- ISBN-13: 9781483258911
- Erscheinungsdatum: 10.07.2014
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