- We will send in 10–14 business days.
/images/books/2983949/9780262530880.jpg)
- Publisher: MIT Press
- ISBN-10: 0262530880
- ISBN-13: 9780262530880
- Format: 15.2 x 22.6 x 2.3 cm, softcover
- Language: English
- SAVE -10% with code: EXTRA
Machine Learning (e-book) (used book) | bookbook.eu
Reviews
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
EXTRA 10 % discount with code: EXTRA
The promotion ends in 17d.11:56:24
The discount code is valid when purchasing from 10 €. Discounts do not stack.
- Publisher: MIT Press
- ISBN-10: 0262530880
- ISBN-13: 9780262530880
- Format: 15.2 x 22.6 x 2.3 cm, softcover
- Language: English English
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
Reviews