Data Perturbation for Escaping Local Maxima in Learning

Data Perturbation for Escaping Local Maxima in Learning (2002)

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by Gal Elidan , Matan Ninio , Nir Friedman , Dale Schuurmans

Venue:	IN AAAI
Citations:	29 - 3 self

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@INPROCEEDINGS{Elidan02dataperturbation,
    author = {Gal Elidan and Matan Ninio and Nir Friedman and Dale Schuurmans},
    title = {Data Perturbation for Escaping Local Maxima in Learning},
    booktitle = {IN AAAI},
    year = {2002},
    pages = {132--139},
    publisher = {}
}

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Abstract

Almost all machine learning algorithms---be they for regression, classification or density estimation---seek hypotheses that optimize a score on training data. In most interesting cases, however, full global optimization is not feasible and local search techniques are used to discover reasonable solutions. Unfortunately,

Citations

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