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  Vicinal Risk Minimization

Chapelle, O., Weston, J., Bottou, L., & Vapnik, V. (2001). Vicinal Risk Minimization. Advances in Neural Information Processing Systems 13, 416-422.

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 Creators:
Chapelle, O1, Author           
Weston, J1, Author           
Bottou, L, Author
Vapnik, V, Author
Affiliations:
1External Organizations, ou_persistent22              

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 Abstract: The Vicinal Risk Minimization principle establishes a bridge between generative models and methods derived from the Structural Risk Minimization Principle such as Support Vector Machines or Statistical Regularization. We
explain how VRM provides a framework which integrates a number of existing algorithms, such as Parzen windows, Support Vector Machines, Ridge Regression, Constrained Logistic Classifiers and Tangent-Prop. We then show how the
approach implies new algorithms for solving problems usually associated with generative models. New algorithms are described for dealing with pattern recognition problems with very different pattern distributions and dealing
with unlabeled data. Preliminary empirical results are presented.

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 Dates: 2001-04
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: 2163
 Degree: -

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Title: Fourteenth Annual Neural Information Processing Systems Conference (NIPS 2000)
Place of Event: Denver, CO, USA
Start-/End Date: 2000-11-27 - 2000-12-02

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Title: Advances in Neural Information Processing Systems 13
Source Genre: Journal
 Creator(s):
Leen, TK, Editor
Dietterich, TG, Editor
Tresp, V, Editor
Affiliations:
-
Publ. Info: Cambridge, MA, USA : MIT Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 416 - 422 Identifier: ISBN: 0-262-12241-3