Machine Learning: Paradigms and Methods

ISBN 10: 0262530880 / ISBN 13: 9780262530880

Edité par MIT Press, 1990

Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less 0.15. N° de réf. du vendeur G0262530880I3N00

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

À propos de l?auteur: Jaime G. Carbonell is Professor of Computer Science at Carnegie-Mellon University.

Détails bibliographiques

Titre : Machine Learning: Paradigms and Methods
Éditeur : MIT Press
Date d'édition : 1990
Reliure : Paperback
Etat : Good
Etat de la jaquette : No Jacket