Machine Learning 1989 Sixth International Conference: Cornell University Ithaca, New York June 26-27, 1989

International Workshop on Machine Learning 1989 (Cornell University)/ Segre, Alberto Maria (Editor)/ Segre, Alberto Maria

ISBN 10: 1558600361 / ISBN 13: 9781558600362

Published by Morgan Kaufmann Pub, 1989

About this Item

510 pages. 10.75x8.25x1.25 inches. In Stock.

Seller Inventory # zk1558600361

Bibliographic Details

• Title: Machine Learning 1989 Sixth International Conference: Cornell University Ithaca, New York June 26-27, 1989
• Publisher: Morgan Kaufmann Pub
• Publication Date: 1989
• Language: English
• ISBN 10: 1558600361
• ISBN 13: 9781558600362
• Binding: Paperback
• Condition: Brand New
• Weight: 1.23 kilograms

About this title

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.