Machine Learning: An Artificial Intelligence Approach

Michalski, Ryszard S. (Author), Carbonell, J. G. (Author)

ISBN 10: 0935382054 / ISBN 13: 9780935382051

Published by Morgan Kaufmann Pub, 1983

About this Item

Book has mild wear to covers with surface scratches and rubbing to back, modest wear to edges and corners with scuffs and light bumps. otherwise in very good condition, strong binding, bright pages with unmarked text; some cosmetic wear but an overall sturdy copy with clean interiors.

Seller Inventory # 1031462

Bibliographic Details

• Title: Machine Learning: An Artificial Intelligence Approach
• Publisher: Morgan Kaufmann Pub
• Publication Date: 1983
• Language: English
• ISBN 10: 0935382054
• ISBN 13: 9780935382051
• Binding: Hardcover
• Condition: Very Good
• Seller catalogs:
  • Engineering Black/Grey

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.