Incorporating invariances in support vector learning machines

Schölkopf, B; Burges, C; Vapnik, V

doi:10.1007/3-540-61510-5_12

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Incorporating invariances in support vector learning machines

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Schölkopf, B
Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Schölkopf, B., Burges, C., & Vapnik, V. (1996). Incorporating invariances in support vector learning machines. In C. von der Malsburg, W. von Seelen, J. Vorbrüggen, & B. Sendhoff (Eds.), Artificial Neural Networks: ICANN 96: 1996 International Conference Bochum, Germany, July 16–19, 1996 (pp. 47-52). Berlin, Germany: Springer.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-EB50-A

Abstract

Developed only recently, support vector learning machines achieve high generalization ability by minimizing a bound on the expected test error; however, so far there existed no way of adding knowledge about invariances of a classification problem at hand. We present a method of incorporating prior knowledge about transformation invariances by applying transformations to support vectors, the training examples most critical for determining the classification boundary.