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Machine Learning: Paradigms and Methods (Special Issues of Artificial Intelligence) (Bradford - Special Issues of AI; An Inte)

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Machine Learning: Paradigms and Methods (Special Issues of Artificial Intelligence) (Bradford - Special Issues of AI; An Inte) [Paperback]

J Carbonell (Author)

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Product details

Paperback: 400 pages
Publisher: MIT Press; 1st, First Edition edition (25 April 1990)
Language English
ISBN-10: 0262530880
ISBN-13: 978-0262530880
Product Dimensions: 22.6 x 15.2 x 2.5 cm

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Product Description

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.The contributors are: 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.Jaime G. Carbonell is Professor of Computer Science at Carnegie-Mellon University.

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