@inproceedings{4168,
  title = {Cross-Validation Optimization for Large Scale Hierarchical
  Classification Kernel Methods},
  journal = {Advances in Neural Information Processing Systems 19: Proceedings of the 2006 Conference},
  booktitle = {Advances in Neural Information Processing Systems 19},
  abstract = {We propose a highly efficient framework for kernel multi-class models with a large and structured set of classes. Kernel parameters are learned automatically by maximizing the cross-validation log likelihood, and
  predictive probabilities are estimated. We demonstrate our
  approach on large scale text classification tasks with hierarchical class structure, achieving state-of-the-art results in an order of magnitude less time than previous work.},
  pages = {1233-1240},
  editors = {Sch{\"o}lkopf, B. , J. Platt, T. Hofmann},
  publisher = {MIT Press},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Cambridge, MA, USA},
  month = sep,
  year = {2007},
  author = {Seeger, M.},
  month_numeric = {9}
}