|
![]() |
|||
|
||||
OverviewMultistrategy 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. Full Product DetailsAuthor: Ryszard S. Michalski (George Mason University) , George TecuciPublisher: Elsevier Science & Technology Imprint: Morgan Kaufmann ISBN: 9781493303526ISBN 10: 149330352 Pages: 782 Publication Date: 31 December 1993 Audience: Professional and scholarly , Professional & Vocational Format: Paperback Publisher's Status: Active Availability: In Print ![]() This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us. Table of ContentsPart One General Issues Part Two Theory Revision Part Three Cooperative Integration Part Four Symbolic and Subsymbolic Learning Part Five Special Topics and ApplicationsReviewsAuthor InformationTab Content 6Author Website:Countries AvailableAll regions |