Proceedings of the ... Annual ACM Conference on Computational Learning Theory
Title | Proceedings of the ... Annual ACM Conference on Computational Learning Theory PDF eBook |
Author | |
Publisher | |
Pages | 326 |
Release | 1998 |
Genre | Machine learning |
ISBN |
Proceedings of the Seventh Annual ACM Conference on Computational Learning Theory
Title | Proceedings of the Seventh Annual ACM Conference on Computational Learning Theory PDF eBook |
Author | |
Publisher | |
Pages | 380 |
Release | 1994 |
Genre | Computers |
ISBN |
Proceedings of the ... Annual Conference on Computational Learning Theory
Title | Proceedings of the ... Annual Conference on Computational Learning Theory PDF eBook |
Author | |
Publisher | |
Pages | 348 |
Release | 1999 |
Genre | Computational learning theory |
ISBN |
An Introduction to Computational Learning Theory
Title | An Introduction to Computational Learning Theory PDF eBook |
Author | Michael J. Kearns |
Publisher | MIT Press |
Pages | 230 |
Release | 1994-08-15 |
Genre | Computers |
ISBN | 9780262111935 |
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.
A Probabilistic Theory of Pattern Recognition
Title | A Probabilistic Theory of Pattern Recognition PDF eBook |
Author | Luc Devroye |
Publisher | Springer Science & Business Media |
Pages | 631 |
Release | 2013-11-27 |
Genre | Mathematics |
ISBN | 1461207118 |
A self-contained and coherent account of probabilistic techniques, covering: distance measures, kernel rules, nearest neighbour rules, Vapnik-Chervonenkis theory, parametric classification, and feature extraction. Each chapter concludes with problems and exercises to further the readers understanding. Both research workers and graduate students will benefit from this wide-ranging and up-to-date account of a fast- moving field.
Computational Learning Theory
Title | Computational Learning Theory PDF eBook |
Author | Paul Vitanyi |
Publisher | Springer Science & Business Media |
Pages | 442 |
Release | 1995-02-23 |
Genre | Computers |
ISBN | 9783540591191 |
This volume presents the proceedings of the Second European Conference on Computational Learning Theory (EuroCOLT '95), held in Barcelona, Spain in March 1995. The book contains full versions of the 28 papers accepted for presentation at the conference as well as three invited papers. All relevant topics in fundamental studies of computational aspects of artificial and natural learning systems and machine learning are covered; in particular artificial and biological neural networks, genetic and evolutionary algorithms, robotics, pattern recognition, inductive logic programming, decision theory, Bayesian/MDL estimation, statistical physics, and cryptography are addressed.
Computational Learning Theory
Title | Computational Learning Theory PDF eBook |
Author | David Helmbold |
Publisher | Springer Science & Business Media |
Pages | 639 |
Release | 2001-07-04 |
Genre | Computers |
ISBN | 3540423435 |
This book constitutes the refereed proceedings of the 14th Annual and 5th European Conferences on Computational Learning Theory, COLT/EuroCOLT 2001, held in Amsterdam, The Netherlands, in July 2001. The 40 revised full papers presented together with one invited paper were carefully reviewed and selected from a total of 69 submissions. All current aspects of computational learning and its applications in a variety of fields are addressed.