Machine learning : paradigms and methods

Author(s)

• Carbonell, Jaime Guillermo

Bibliographic Information

Machine learning : paradigms and methods

edited by J.G. Carbonell

（Bradford book）

MIT Press, 1990

Note

Reprinted from Artificial intelligence : An International journal, v. 40 (1989), no. 1-3, Sept. 1989

Includes bibliographical references and indexes

Description and Table of Contents

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. 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

by "Nielsen BookData"

Details

• NCID: BA10183319
• ISBN:
  • 0262530880
• LCCN: 89013641
• Country Code: us
• Title Language Code: eng
• Text Language Code: eng
• Place of Publication: Cambridge, Mass.
• Pages/Volumes: 394 p.
• Size: 23 cm
• Classification:
  • LCC : Q325.5
  • DC20 : 006.3/1
• Subject Headings:
  • LCSH : Machine learning
  • LCSH : Artificial intelligence
• Parent Bibliography ID:
  • BA00245453