Incorporating Invariances in Non-Linear Support Vector Machines

Chapelle, O; Schölkopf, B

Item

ITEM ACTIONSEXPORT

Add to Basket

Local TagsRelease HistoryDetailsSummary

Released

Conference Paper

Incorporating Invariances in Non-Linear Support Vector Machines

MPS-Authors

/persons/resource/persons84193

Schölkopf, B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

External Resource

https://papers.nips.cc/paper/2024-incorporating-invariances-in-non-linear-support-vector-machines.pdf
(Publisher version)

Fulltext (restricted access)

There are currently no full texts shared for your IP range.

Fulltext (public)

There are no public fulltexts stored in PuRe

Supplementary Material (public)

There is no public supplementary material available

Citation

Chapelle, O., & Schölkopf, B. (2002). Incorporating Invariances in Non-Linear Support Vector Machines. In T. Dietterich, S. Becker, & Z. Ghahramani (Eds.), Advances in Neural Information Processing Systems 14 (pp. 609-616). Cambridge, MA, USA: MIT Press.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-DF0B-7

Abstract

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.