Training invariant support vector machines

DeCoste, D; Schölkopf, B

doi:10.1023/A:1012454411458

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Training invariant support vector machines

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

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https://link.springer.com/content/pdf/10.1023/A:1012454411458.pdf
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Citation

DeCoste, D., & Schölkopf, B. (2002). Training invariant support vector machines. Machine Learning, 46(1-3), 161-190. doi:10.1023/A:1012454411458.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-E06A-A

Abstract

Practical experience has shown that in order to obtain the best possible performance, prior knowledge about invariances of a classification
problem at hand ought to be incorporated into the training procedure. We describe and review all known methods for doing so in support vector machines,
provide experimental results, and discuss their respective merits. One of the significant new results reported in this work is our recent achievement of the
lowest reported test error on the well-known MNIST digit recognition benchmark task, with SVM training times that are also significantly faster than
previous SVM methods.