@techreport{3863,
  title = {Cross-Validation Optimization for Structured Hessian Kernel Methods},
  abstract = {We address the problem of learning hyperparameters in kernel methods for
  which the Hessian of the objective is structured. We propose an approximation
  to the cross-validation log likelihood whose gradient can be computed
  analytically, solving the hyperparameter learning problem efficiently
  through nonlinear optimization. Crucially, our learning method is based
  entirely on matrix-vector multiplication primitives with the kernel
  matrices and their derivatives, allowing straightforward specialization to
  new kernels or to large datasets. When applied to the problem of multi-way
  classification, our method scales linearly in the number of classes and
  gives rise to state-of-the-art results on a remote imaging task.},
  organization = {Max-Planck-Gesellschaft},
  institution = {Max-Planck Institute for Biological Cybernetics, Tübingen, Germany},
  school = {Biologische Kybernetik},
  month = feb,
  year = {2006},
  slug = {3863},
  author = {Seeger, M. and Chapelle, O.},
  month_numeric = {2}
}