Learning Models Over The Boolean Domain Avrim L. Blum  Home/Search
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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... (Update)
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BibTeX entry: (Update)
@misc{ over-separating,
author = "Learning Models Over",
title = "Separating Distribution-Free And Mistake-Bound",
url = "citeseer.ist.psu.edu/752403.html" }
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