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Incorporating Invariances in Non-Linear Support Vector Machines

2002

Conference Paper

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The choice of an SVM kernel corresponds to the choice of a representation of the data in a feature space and, to improve performance, it should therefore incorporate prior knowledge such as known transformation invariances. We propose a technique which extends earlier work and aims at incorporating invariances in nonlinear kernels. We show on a digit recognition task that the proposed approach is superior to the Virtual Support Vector method, which previously had been the method of choice.

Author(s): Chapelle, O. and Schölkopf, B.
Book Title: Advances in Neural Information Processing Systems 14
Journal: Advances in Neural Information Processing Systems
Pages: 609-616
Year: 2002
Month: September
Day: 0
Editors: TG Dietterich and S Becker and Z Ghahramani
Publisher: MIT Press

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

Event Name: 15th Annual Neural Information Processing Systems Conference (NIPS 2001)
Event Place: Vancouver, BC, Canada

Address: Cambridge, MA, USA
Digital: 0
ISBN: 0-262-04208-8
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF
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BibTex

@inproceedings{1820,
  title = {Incorporating Invariances in Non-Linear Support Vector Machines },
  author = {Chapelle, O. and Sch{\"o}lkopf, B.},
  journal = {Advances in Neural Information Processing Systems},
  booktitle = {Advances in Neural Information Processing Systems 14},
  pages = {609-616},
  editors = {TG Dietterich and S Becker and Z Ghahramani},
  publisher = {MIT Press},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Cambridge, MA, USA},
  month = sep,
  year = {2002},
  month_numeric = {9}
}