Separating Models of Learning from Correlated and Uncorrelated Data
The Journal of Machine Learning Research
On exact learning from random walk
ALT'06 Proceedings of the 17th international conference on Algorithmic Learning Theory
On attribute efficient and non-adaptive learning of parities and DNF expressions
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Separating models of learning from correlated and uncorrelated data
COLT'05 Proceedings of the 18th annual conference on Learning Theory
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We consider a model of learning Boolean functions from examples generated by a uniform random walk on {0, 1}n. We give a polynomial time algorithm for learning decision trees and DNF formulas in this model. This is the first efficient algorithm for learning these classes in a natural passive learning model where the learner has no influence overthe choice of examples used for learning.