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.
CITATION STYLE
Schölkopf, B., Burges, C., & Vapnik, V. (1996). Incorporating invariances in support vector learning machines. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1112 LNCS, pp. 47–52). Springer Verlag. https://doi.org/10.1007/3-540-61510-5_12
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