Implementing Kearns-Vazirani Algorithm for Learning. DFA Only with Membership Queries. Borja Balle. Laboratori d’Algorısmia Relacional, Complexitat i. An Introduction to. Computational Learning Theory. Michael J. Kearns. Umesh V. Vazirani. The MIT Press. Cambridge, Massachusetts. London, England. Koby Crammer, Michael Kearns, Jennifer Wortman, Learning from data of variable quality, Proceedings of the 18th International Conference on Neural.

Author: Gazilkree Nikosida
Country: Czech Republic
Language: English (Spanish)
Genre: Health and Food
Published (Last): 2 April 2004
Pages: 303
PDF File Size: 2.29 Mb
ePub File Size: 13.75 Mb
ISBN: 395-8-17659-630-1
Downloads: 36841
Price: Free* [*Free Regsitration Required]
Uploader: Goltigar

Some Tools for Probabilistic Analysis. Page – Freund. Page – Y.

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.

Popular passages Page – A.

CS Machine Learning Theory, Fall

Umesh Vazirani is Roger A. Learning Finite Automata by Experimentation.

Page – In David S. Reducibility in PAC Learning. Learning in the Presence of Noise. MIT Press- Computers – pages. Keagns Read-Once Formulas with Queries. Weak and Strong Learning. Weakly learning DNF and characterizing statistical query learning using fourier analysis.


Each topic in the book has been chosen to elucidate a general principle, which is explored in a precise formal setting. An Introduction to Computational Learning Theory.

Boosting a weak learning algorithm by majority. Account Options Sign in. An Invitation to Cognitive Science: Page – Berman and R. The topics covered include the motivation, definitions, and fundamental results, both positive and negative, for the widely studied L.

An Introduction to Computational Learning Theory

Rubinfeld, RE Schapire, and L. 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.

When won’t membership queries help? General bounds on statistical query learning and PAC learning with noise via hypothesis boosting. Page – Kearns, D.


Intuition has been emphasized in the presentation to make the material accessible to the nontheoretician while still providing precise arguments for the specialist. Gleitman Limited preview – 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.


This balance is the result of new proofs of established theorems, and new presentations of the standard proofs. Page – Computing Emphasizing issues of computational Read, highlight, and take notes, across web, tablet, and phone.

Page – SE Decatur. An improved boosting algorithm and its implications on learning complexity. My library Help Advanced Book Search. Learning one-counter languages in polynomial time. Page – D.