Michael Kearns, Yishay Mansour, Andrew Y. Ng, Dana Ron  Home/Search
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Abstract: this paper is to provide such a comparison, and more importantly, to describe the general conclusions to which it has led. Relying on evidence that is divided between controlled experimental results and related formal analysis, we compare three well-known model selection algorithms. We attempt to identify their relative and absolute strengths and weaknesses, and we provide some general methods for analyzing the behavior and performance of model selection algorithms. Our hope is that these... (Update)
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BibTeX entry: (Update)
M. Kearns, Y. Mansour, A. Y. Ng and D. Ron, "An Experimental and Theoretical Comparison of Model Selection Methods", in Proceedings of the seventh workshop on Computational Learning Theory ", ACM Press, http://citeseer.ist.psu.edu/667903.html More
@inproceedings{ kearns95experimental,
author = "Michael J. Kearns and Yishay Mansour and Andrew Y. Ng and Dana Ron",
title = "An Experimental and Theoretical Comparison of Model Selection Methods",
booktitle = "Computational Learing Theory",
pages = "21-30",
year = "1995",
url = "citeseer.ist.psu.edu/667903.html" }
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139 Stochastic complexity and modeling (context) - Rissanen - 1986
101 Minimum complexity density estimation (context) - Barron, Cover - 1991
58 Statistical mechanics of learning from examples (context) - Seung, Sompolinsky et al. - 1992
23 Asymptotics for and against cross-validation (context) - Stone - 1977
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