Machine Learning Proceedings 1989 - Couverture souple

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9781558600362: Machine Learning Proceedings 1989
Couverture souple
ISBN 10 : 1558600361 ISBN 13 : 9781558600362
Editeur : Morgan Kaufmann, 1989

Synopsis

Proceedings of the Sixth International Workshop on Machine Learning covers the papers presented at the Sixth International Workshop of Machine Learning, held at Cornell University, Ithaca, New York (USA) on June 26-27, 1989. The book focuses on the processes, methodologies, techniques, and approaches involved in machine learning. The selection first offers information on unifying themes in empirical and explanation-based learning; integrated learning of concepts with both explainable and conventional aspects; conceptual clustering of explanations; and tight integration of deductive and inductive learning. The text then examines multi-strategy learning in nonhomogeneous domain theories; description of preference criterion in constructive learning; and combining case-based reasoning, explanation-based learning, and learning from instruction. Discussions focus on causal explanation of actions, constructive learning, learning in a weak theory domain, learning problem, and individual criteria and their relationships. The book elaborates on learning from plausible explanations, augmenting domain theory for explanation-based generalization, reducing search and learning goal preferences, and using domain knowledge to improve inductive learning algorithms for diagnosis. The selection is a dependable reference for researchers interested in the dynamics of machine learning.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

Présentation de l'éditeur

Proceedings of the Sixth International Workshop on Machine Learning covers the papers presented at the Sixth International Workshop of Machine Learning, held at Cornell University, Ithaca, New York (USA) on June 26-27, 1989. The book focuses on the processes, methodologies, techniques, and approaches involved in machine learning. The selection first offers information on unifying themes in empirical and explanation-based learning; integrated learning of concepts with both explainable and conventional aspects; conceptual clustering of explanations; and tight integration of deductive and inductive learning. The text then examines multi-strategy learning in nonhomogeneous domain theories; description of preference criterion in constructive learning; and combining case-based reasoning, explanation-based learning, and learning from instruction. Discussions focus on causal explanation of actions, constructive learning, learning in a weak theory domain, learning problem, and individual criteria and their relationships. The book elaborates on learning from plausible explanations, augmenting domain theory for explanation-based generalization, reducing search and learning goal preferences, and using domain knowledge to improve inductive learning algorithms for diagnosis. The selection is a dependable reference for researchers interested in the dynamics of machine learning.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
Morgan Kaufmann
Date d'édition
1989
Langue
anglais
ISBN 10 
1558600361
ISBN 13 
9781558600362
Reliure
Broché
Nombre de pages
520
Éditeur
Machine Learning Machine Learning
Coordonnées du fabricant
non disponible
Personne responsable
non disponible