Machine Learning: An Artificial Intelligence Approach

Michalski, Ryszard S.; Carbonell, J. G.

ISBN 10: 0935382054 / ISBN 13: 9780935382051

Published by Morgan Kaufmann Pub, 1983

About this Item

A copy that has been read, remains in good condition. All pages are intact, and the cover is intact. The spine and cover show signs of wear. Pages can include notes and highlighting and show signs of wear, and the copy can include "From the library of" labels or previous owner inscriptions. 100% GUARANTEE! Shipped with delivery confirmation, if you're not satisfied with purchase please return item for full refund. Ships via media mail.

Seller Inventory # OTV.0935382054.G

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: good

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.