On exact learning from random walk

Authors:
Nader H. Bshouty;Iddo Bentov
Affiliations:
Department of Computer Science, Technion, Haifa, Israel;Department of Computer Science, Technion, Haifa, Israel
Venue:
ALT'06 Proceedings of the 17th international conference on Algorithmic Learning Theory
Year:
2006

Citing 11
Cited 0

On the learnability of Boolean formulae

STOC '87 Proceedings of the nineteenth annual ACM symposium on Theory of computing
Prediction-preserving reducibility

Journal of Computer and System Sciences - 3rd Annual Conference on Structure in Complexity Theory, June 14–17, 1988
Learning monotone ku DNF formulas on product distributions

COLT '91 Proceedings of the fourth annual workshop on Computational learning theory
On learning ring-sum-expansions

SIAM Journal on Computing
Simple learning algorithms using divide and conquer

Computational Complexity
Queries and Concept Learning

Machine Learning
Learning Quickly When Irrelevant Attributes Abound: A New Linear-Threshold Algorithm

Machine Learning
Queries and Concept Learning

Machine Learning
Exploiting random walks for learning

Information and Computation
Learning DNF from Random Walks

FOCS '03 Proceedings of the 44th Annual IEEE Symposium on Foundations of Computer Science
Learning functions of k relevant variables

Journal of Computer and System Sciences - Special issue: STOC 2003

Quantified Score

Hi-index	0.00

Visualization

Abstract

We consider a few particular exact learning models based on a random walk stochastic process, and thus more restricted than the well known general exact learning models. We give positive and negative results as to whether learning in these particular models is easier than in the general learning models.