@article{3512,
  title = {Learning the Kernel with Hyperkernels},
  journal = {Journal of Machine Learning Research},
  abstract = {This paper addresses the problem of choosing a kernel suitable for
  estimation with a Support Vector
  Machine, hence further automating machine learning.
  This goal is achieved by defining a Reproducing Kernel Hilbert
  Space on the space of kernels itself. Such a formulation leads to a
  statistical estimation problem similar to the problem of minimizing
  a regularized risk functional.
  We state the equivalent
  representer theorem for the choice of kernels and present a
  semidefinite programming formulation of the resulting optimization
  problem. Several recipes for constructing hyperkernels are provided, as
  well as the details of common machine learning problems. Experimental
  results for classification, regression and novelty
  detection on UCI data show the feasibility of our approach.},
  volume = {6},
  pages = {1043-1071},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = jul,
  year = {2005},
  slug = {3512},
  author = {Ong, CS. and Smola, A. and Williamson, R.},
  month_numeric = {7}
}