Separating Distribution-Free And Mistake-Bound Learning Models Over The Boolean Domain (1990)
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| Venue: | SIAM J. COMPUT |
| Citations: | 21 - 2 self |
BibTeX
@ARTICLE{Blum90separatingdistribution-free,
author = {Avrim L. Blum},
title = {Separating Distribution-Free And Mistake-Bound Learning Models Over The Boolean Domain},
journal = {SIAM J. COMPUT},
year = {1990},
volume = {23},
pages = {211--218}
}
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Abstract
Two of the most commonly used models in computational learning theory are the distribution-free model in which examples are chosen from a fixed but arbitrary distribution, and the absolute mistake-bound model in which examples are presented in an arbitrary order. Over the Boolean domain , it is known that if the learner is allowed unlimited computational resources then any concept class learnable in one model is also learnable in the other. In addition, any polynomial-time learning algorithm for a concept class in the mistake-bound model can be transformed into one that learns the class in the distribution-free model. This paper
