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Vicinal Risk Minimization


Conference Paper


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.

Author(s): Chapelle, O. and Weston, J. and Bottou, L. and Vapnik, V.
Book Title: Advances in Neural Information Processing Systems 13
Journal: Advances in Neural Information Processing Systems
Pages: 416-422
Year: 2001
Month: April
Day: 0
Editors: Leen, T.K. , T.G. Dietterich, V. Tresp
Publisher: MIT Press

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

Event Name: Fourteenth Annual Neural Information Processing Systems Conference (NIPS 2000)
Event Place: Denver, CO, USA

Address: Cambridge, MA, USA
Digital: 0
ISBN: 0-262-12241-3
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF


  title = {Vicinal Risk Minimization},
  author = {Chapelle, O. and Weston, J. and Bottou, L. and Vapnik, V.},
  journal = {Advances in Neural Information Processing Systems},
  booktitle = {Advances in Neural Information Processing Systems 13},
  pages = {416-422},
  editors = {Leen, T.K. , T.G. Dietterich, V. Tresp},
  publisher = {MIT Press},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Cambridge, MA, USA},
  month = apr,
  year = {2001},
  month_numeric = {4}