Machine Learning: Paradigms and Methods (Special Issues of Artificial Intelligence)

ISBN 10: 0262530880 / ISBN 13: 9780262530880

Published by MIT Press, 1990

About this Item

Seller Inventory # ABLIING23Feb2215580084584

Bibliographic Details

• Title: Machine Learning: Paradigms and Methods (Special Issues of Artificial Intelligence)
• Publisher: MIT Press
• Publication Date: 1990
• Language: English
• ISBN 10: 0262530880
• ISBN 13: 9780262530880
• Binding: Soft cover
• Condition: New

About this title

Synopsis

Having played a central role at the inception of artificial intelligence research, machine learning has recently reemerged as a major area of study at the very core of the subject. Solid theoretical foundations are being constructed. Machine learning methods are being integrated with powerful performance systems, and practical applications; based on established techniques are emerging.Machine Learning unifies the field by bringing together and clearly explaining the major successful paradigms for machine learning: inductive approaches, explanation-based learning, genetic algorithms, and connectionist learning methods. Each paradigm is presented in depth, providing historical perspective but focusing on current research and potential applications.

Contributors
John R. Anderson, L. B. Booker, John. H. Gennari, Jaime G. Carbonell, Oren Etzioni, Doug Fisher, Yolanda Gil, D. E. Goldberg, Gerald E. Hinton, J. H. Holland, Craig A Knoblock, Daniel. R. Kuokka, Pat Langley, David B. Leake, Steve Minton, Jack Mostow, Roger C. Schank, and Jan M. Zytkow

About the Author

Jaime G. Carbonell is Professor of Computer Science at Carnegie-Mellon University.