An Introduction to Computational Learning Theory

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An Introduction to Computational Learning Theory
By Michael J. Kearns, Umesh Vazirani
An Introduction to Computational Learning Theory Details:
Amazon Sales Rank: #606796 in Books
Published on: 1994-08-15
Original language: English
Number of items: 1
Dimensions: .72" h x 7.35" w x 9.30" l, 1.11 pounds
Binding: Hardcover
221 pages
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
An Introduction to Computational Learning Theory By Michael J. Kearns, Umesh Vazirani
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