MACHINE LEARNING an Artificial Intelligence Approach

Michalski, Carbonell, & Mitchell (ed.)

ISBN 10: 0935382054 / ISBN 13: 9780935382051

Published by Tioga Publishing Company, Palo Alto, 1983

About this Item

Hardcover; 572 pages. Paper-bound hardcovers, black with white text. More or less scuffed over both covers, worn corners & spine edges Clean white endpapers. The pages are clean, white, unmarked. VERY GOOD.

Seller Inventory # 217716

Bibliographic Details

• Title: MACHINE LEARNING an Artificial Intelligence Approach
• Publisher: Tioga Publishing Company, Palo Alto
• Publication Date: 1983
• ISBN 10: 0935382054
• ISBN 13: 9780935382051
• Binding: Hardcover
• Condition: Very Good
• Dust Jacket Condition: No Jacket
• Book Type: Book
• Seller catalogs:
  • Science and Technology

About this title

Synopsis

Multistrategy learning is one of the newest and most promising research directions in the development of machine learning systems. The objectives of research in this area are to study trade-offs between different learning strategies and to develop learning systems that employ multiple types of inference or computational paradigms in a learning process. Multistrategy systems offer significant advantages over monostrategy systems. They are more flexible in the type of input they can learn from and the type of knowledge they can acquire. As a consequence, multistrategy systems have the potential to be applicable to a wide range of practical problems. This volume is the first book in this fast growing field. It contains a selection of contributions by leading researchers specializing in this area.

From the Back Cover

Multistrategy learning is one of the newest and most promising research directions in the development of machine learning systems. The objectives of research in this area are to study trade-offs between different learning strategies and to develop learning systems that employ multiple types of inference or computational paradigms in a learning process. Multistrategy systems offer significant advantages over monostrategy systems. They are more flexible in the type of input they can learn from and the type of knowledge they can acquire. As a consequence, multistrategy systems have the potential to be applicable to a wide range of practical problems. This volume is the first book in this fast growing field. It contains a selection of contributions by leading researchers specializing in this area.

See below for earlier volumes in the series.