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Conference Paper

Incorporating invariances in support vector learning machines

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84193

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

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84987

Vapnik,  V
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. Artificial Neural Networks --- ICANN‘96, 47-52.


Cite as: http://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.