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Emphasizing issues of computational efficiency, Michael Kearns and Umesh Vazirani introduce a number of central topics in computational learning theory for researchers and students in artificial intelligence, neural networks, theoretical computer science, and statistics. Emphasizing issues of computational efficiency, Michael Kearns and Umesh Vazirani introduce a number of central topics in computational learning theory for researchers and students in artificial intelligence, neural networks, theoretical computer science, and statistics. Computational learning theory is a new and rapidly expanding area of research that examines formal models of induction with the goals of discovering the common methods underlying efficient learning algorithms and identifying the computational impediments to learning. Each topic in the book has been chosen to elucidate a general principle, which is explored in a precise formal setting. Intuition has been emphasized in the presentation to make the material accessible to the nontheoretician while still providing precise arguments for the specialist. This balance is the result of new proofs of established theorems, and new presentations of the standard proofs. The topics covered include the motivation, definitions, and fundamental results, both positive and negative, for the widely studied L. G. Valiant model of Probably Approximately Correct Learning; Occam's Razor, which formalizes a relationship between learning and data compression; the Vapnik-Chervonenkis dimension; the equivalence of weak and strong learning; efficient learning in the presence of noise by the method of statistical queries; relationships between learning and cryptography, and the resulting computational limitations on efficient learning; reducibility between learning problems; and algorithms for learning finite automata from active experimentation.
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An Introduction to Computational Learning Theory - MIT Press ~ Computational learning theory is a new and rapidly expanding area of research that examines formal models of induction with the goals of discovering the common methods underlying efficient learning algorithms and identifying the computational impediments to learning. Each topic in the book has been chosen to elucidate a general principle, which .
Read Download An Introduction To Computational Learning ~ Read Online An Introduction To Computational Learning Theory Mit Press and Download An Introduction To Computational Learning Theory Mit Press book full in PDF formats. . This book provides a comprehensive introduction to the computational material that forms the underpinnings of the currently evolving set of brain models.
An Introduction To Computational Learning Theory Mit Press ~ Download An Introduction To Computational Learning Theory Mit Press eBook in PDF, EPUB, . DOWNLOAD NOW » Emphasizing issues . This book provides a comprehensive introduction to the computational material that forms the underpinnings of the currently evolving set of brain models.
An Introduction to Computational Learning Theory / MIT ~ Book Abstract: Emphasizing issues of computational efficiency, Michael Kearns and Umesh Vazirani introduce a number of central topics in computational learning theory for researchers and students in artificial intelligence, neural networks, theoretical computer science, and statistics.Computational learning theory is a new and rapidly expanding area of research that examines formal models of .
An introduction to computational learning theory / Guide books ~ The book's writing style is clear and pleasant, reflecting the current trend toward intuitive, philosophical presentations of complex technical matters. Although readers should not expect to find plug-and-play algorithms, the book is recommended to everyone as a solid introduction to the theoretical aspects of computational learning.
An Introduction to Computational Learning Theory / The MIT ~ Emphasizing issues of computational efficiency, Michael Kearns and Umesh Vazirani introduce a number of central topics in computational learning theory for researchers and students in artificial intelligence, neural networks, theoretical computer science, and statistics. Emphasizing issues of computational efficiency, Michael Kearns and Umesh Vazirani introduce a number of central topics in .
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Perceptrons : An Introduction to Computational - MIT Press ~ Then the pendulum swung back, and machine learning became the fastest-growing field in computer science. Minsky and Papert's insistence on its theoretical foundations is newly relevant. Perceptrons —the first systematic study of parallelism in computation—marked a historic turn in artificial intelligence, returning to the idea that intelligence might emerge from the activity of networks of .
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Perceptrons: An Introduction To Computational Geometry By ~ Introduction to Computational Papert s other books include Perceptrons. an introduction to computational Book Reviews Perceptrons. An Introduction to Computational Geometry. Marvin Minsky and Seymour Papert. M.I.T. Press, Cambridge, Mass., 1969. vi + 258 pp., Perceptrons / the mit press An Introduction to Computational Geometry .