The supervised learning no-free-lunch Theorems

The supervised learning no-free-lunch Theorems (2001)

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by David H. Wolpert

Venue:	In Proc. 6th Online World Conference on Soft Computing in Industrial Applications
Citations:	22 - 0 self

BibTeX

@INPROCEEDINGS{Wolpert01thesupervised,
    author = {David H. Wolpert},
    title = {The supervised learning no-free-lunch Theorems},
    booktitle = {In Proc. 6th Online World Conference on Soft Computing in Industrial Applications},
    year = {2001},
    pages = {25--42}
}

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Abstract

Abstract This paper reviews the supervised learning versions of the no-free-lunch theorems in a simplified form. It also discusses the significance of those theorems, and their relation to other aspects of supervised learning.

Citations

511	DM: Statistical Analysis of Finite Mixture Distributions Chichester - Titterington - 1985
463	Stacked generalization - Wolpert - 1992
332	Computer systems that learn - Weiss, Kulikowski - 1991
111	Bayesian back–propagation - Buntine, Weigend - 1991
55	On the Connection Between In-sample Testing and Generalization Error - Wolpert - 1992
37	The relationship between PAC, the statistical physics framework, the Bayesian framework, and the VC framework - Wolpert - 1994
22	On bias plus variance - Wolpert - 1997
21	Priors for infinite networks - Neal - 1994
13	Bayesian backpropagation over i-o functions rather than weights - Wolpert - 1994
12	Reconciling Bayesian and non-Bayesian analysis - Wolpert - 1994
6	The lack of a prior distinction between learning algorithms - Wolpert - 1996
5	Filter likelihoods and exhaustive learning - Wolpert - 1994
2	Priors for in nite networks - Neal - 1994
1	et al. Occam's razor - Blumer - 1987
1	5] Titterington et al. Statistical Analysis of Finite Mixture Distributions - Blumer - 1987