A compression approach to support vector model selection

@techreport{1868,
  title = {A compression approach to support vector model selection},
  abstract = {In this paper we investigate connections between statistical learning
  theory and data compression on the basis of support vector machine
  (SVM) model selection.  Inspired by several generalization bounds we
  construct ``compression coefficients'' for SVMs, which measure the
  amount by which the training labels can be compressed by some
  classification hypothesis. The main idea is to relate the coding
  precision of this hypothesis to the width of the margin of the
  SVM. The compression coefficients connect well known quantities such
  as the radius-margin ratio R^2/rho^2, the eigenvalues of the kernel
  matrix and the number of support vectors. To test whether they are
  useful in practice we ran model selection experiments on several real
  world datasets. As a result we found that compression coefficients can
  fairly accurately predict the parameters for which the test error is
  minimized.},
  number = {101},
  organization = {Max-Planck-Gesellschaft},
  institution = {Max Planck Institute for Biological Cybernetics},
  school = {Biologische Kybernetik},
  year = {2002},
  note = {see more detailed JMLR version},
  author = {von Luxburg, U. and Bousquet, O. and Sch{\"o}lkopf, B.}
}