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Incorporating invariances in support vector learning machines
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.
@inproceedings{796, title = {Incorporating invariances in support vector learning machines}, journal = {Artificial Neural Networks --- ICANN‘96}, booktitle = {Artificial Neural Networks: ICANN 96, LNCS vol. 1112}, 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.}, pages = {47-52}, editors = {C von der Malsburg and W von Seelen and JC Vorbr{\"u}ggen and B Sendhoff}, publisher = {Springer}, organization = {Max-Planck-Gesellschaft}, school = {Biologische Kybernetik}, address = {Berlin, Germany}, month = jul, year = {1996}, note = {volume 1112 of Lecture Notes in Computer Science }, slug = {796}, author = {Sch{\"o}lkopf, B. and Burges, C. and Vapnik, V.}, month_numeric = {7} }
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