@inproceedings{1820,
  title = {Incorporating Invariances in Non-Linear Support Vector Machines },
  journal = {Advances in Neural Information Processing Systems},
  booktitle = {Advances in Neural Information Processing Systems 14},
  abstract = {The choice of an SVM kernel corresponds to the choice of a
  representation of the data in a feature space and, to
  improve performance, it should therefore incorporate prior knowledge such as known transformation invariances. We propose a technique which extends earlier work and aims at incorporating invariances in nonlinear kernels.  We show on a
  digit recognition task that the proposed approach is
  superior to the Virtual Support Vector method, which previously had been the method of choice.},
  pages = {609-616},
  editors = {TG Dietterich and S Becker and Z Ghahramani},
  publisher = {MIT Press},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Cambridge, MA, USA},
  month = sep,
  year = {2002},
  author = {Chapelle, O. and Sch{\"o}lkopf, B.},
  month_numeric = {9}
}