Machine Learning

<p>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.</p><p><b>Contributors<br/></b>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</p>