Machine Learning

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.ContributorsJohn 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