Machine Learning: Paradigms and Methods Jaime G. Carbonell

ISBN: 9780262530880

Published: February 23rd 1990

Paperback

400 pages


Description

Machine Learning: Paradigms and Methods  by  Jaime G. Carbonell

Machine Learning: Paradigms and Methods by Jaime G. Carbonell
February 23rd 1990 | Paperback | PDF, EPUB, FB2, DjVu, talking book, mp3, RTF | 400 pages | ISBN: 9780262530880 | 5.40 Mb

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 learningMoreHaving 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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