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Book 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. 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.

Book details

Book category Books in English Computing & information technology Computer science

• Full title: Machine Learning
• Subtitle: Paradigms and Methods
• Author: Jaime G. Carbonell
• Language: English
• Binding: Paperback
• Number of pages: 400
• EAN: 9780262530880
• ISBN: 9780262530880
• ID: 06516228
• Publisher: MIT Press Ltd
• Weight: 635 g
• Dimensions: 229 × 152 × 24 mm
• Date of publishing: 23. February 1990

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