WorldCat Identities

Kearns, Michael J.

Overview
Works: 10 works in 58 publications in 1 language and 1,882 library holdings
Genres: Conference papers and proceedings  Periodicals 
Roles: Author, Editor
Classifications: Q325.5, 006.3
Publication Timeline
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Most widely held works by Michael J Kearns
An introduction to computational learning theory by Michael J Kearns( Book )

24 editions published between 1994 and 1997 in English and held by 388 WorldCat member libraries worldwide

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
The computational complexity of machine learning by Michael J Kearns( Book )

12 editions published between 1989 and 1990 in English and held by 289 WorldCat member libraries worldwide

We are interested in the phenomenon of efficient learning in the distribution-free model, in the standard polynomial-time sense. Our results include general tools for determining the polynomial-time learnability of a concept-class, an extensive study of efficient learning when errors are present in the examples, and lower bounds on the number of examples required for learning in out model. A centerpiece of the thesis is a series of results demonstrating the computational difficulty of learning a number of well-studied concept classes. These results are obtained by reducing some apparently hard number-theoretic problems from cryptography to the learning problems. The hard-to-learn concept classes include the sets represented by Boolean formulae, deterministic finite automata and a simplified form of neural networks
Advances in neural information processing systems 9 : proceedings of the 1996 conference by NIPS( Book )

15 editions published between 1998 and 1999 in English and held by 82 WorldCat member libraries worldwide

Contains the entire proceedings of the 12 Neural Information Processing Systems conferences from 1988 to 1999
An Introduction to Computational Learning Theory by Michael J Kearns( )

in English and held by 13 WorldCat member libraries worldwide

Learning boolean formulae or finite automata is as hard as factoring by Michael J Kearns( Book )

1 edition published in 1988 in English and held by 4 WorldCat member libraries worldwide

Exact identification of read-once formulas using fixed points of amplification functions by Sally A Goldman( Book )

1 edition published in 1991 in English and held by 1 WorldCat member library worldwide

Abstract: "In this paper we describe a new technique for exactly identifying certain classes of read-once Boolean formulas. The method is based on sampling the input-output behavior of the target formula on a probability distribution which is determined by the fixed point of the formula's amplification function (defined as the probability that a 1 is output by the formula when each input bit is 1 independently with probability p). By performing various statistical tests on easily sampled variants of the fixed-point distribution, we are able to efficiently infer all structural information about any logarithmic-depth formula (with high probability). We apply our results to prove the existence of short universal identification sequences for large classes of formulas
Advances in neural information processing systems 10( )

1 edition published in 1998 in English and held by 1 WorldCat member library worldwide

Nature of the procine intestinal K88 receptor by Michael J Kearns( )

1 edition published in 1979 in English and held by 1 WorldCat member library worldwide

Advances in neural information processing systems 10( Book )

1 edition published in 1998 in English and held by 1 WorldCat member library worldwide

Public affairs : a study and proposal for the Illinois Department of Transportation by Michael J Kearns( Book )

1 edition published in 1971 in English and held by 0 WorldCat member libraries worldwide

 
Audience Level
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Audience Level
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Audience level: 0.37 (from 0.08 for An introdu ... to 1.00 for Public aff ...)

An introduction to computational learning theory
Languages
English (58)

Covers
Advances in neural information processing systems 9 : proceedings of the 1996 conferenceAdvances in neural information processing systems 10Advances in neural information processing systems 10