Machine Learning: A Multistrategy Approach. Volume IV

Michalski, Ryszard S. and Gheorghe Tecuci:

ISBN 10: 1558602518 / ISBN 13: 9781558602519

Published by Morgan Kaufmann Publishers, 1994

About this Item

XI, 782 Seiten, Zust: Gutes Exemplar. Buchkante unten mit Kerben. Mit Vorbesitzereintrag. Schneller Versand und persönlicher Service - jedes Buch händisch geprüft und beschrieben - aus unserem Familienbetrieb seit über 25 Jahren. Eine Rechnung mit ausgewiesener Mehrwertsteuer liegt jeder unserer Lieferungen bei. Wir versenden mit der deutschen Post. Sprache: Englisch Gewicht in Gramm: 1240 gebundene Ausgabe gebundene Ausgabe.

Seller Inventory # 492573

Bibliographic Details

• Title: Machine Learning: A Multistrategy Approach. Volume IV
• Publisher: Morgan Kaufmann Publishers
• Publication Date: 1994
• Language: English
• ISBN 10: 1558602518
• ISBN 13: 9781558602519
• Binding: Hardcover
• Condition: Gut
• Weight: 1240 grams
• Seller catalogs:
  • Naturwissenschaften Informatik

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.