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Abstract: . In this paper we initiate an investigation of generalizations of the Probably Approximately Correct (PAC) learning model that attempt to significantly weaken the target function assumptions. The ultimate goal in this direction is informally termed agnostic learning, in which we make virtually no assumptions on the target function. The name derives from the fact that as designers of learning algorithms, we give up the belief that Nature (as represented by the target function) has a simple or... (Update)
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BibTeX entry: (Update)
M. Kearns, R. Schapire, and L. Sellie. Toward efficient agnostic learning. Machine Learning, 17(2/3):115--142, 1994. http://citeseer.ist.psu.edu/kearns94toward.html More
@inproceedings{ kearns92toward,
author = "Michael J. Kearns and Robert E. Schapire and Linda Sellie",
title = "Toward Efficient Agnostic Learning",
booktitle = "Computational Learing Theory",
pages = "341-352",
year = "1992",
url = "citeseer.ist.psu.edu/kearns94toward.html" }
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