Header logo is

Learning the Kernel with Hyperkernels




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.

Author(s): Ong, CS. and Smola, A. and Williamson, R.
Journal: Journal of Machine Learning Research
Volume: 6
Pages: 1043-1071
Year: 2005
Month: July
Day: 0

Department(s): Empirical Inference
Bibtex Type: Article (article)

Digital: 0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF


  title = {Learning the Kernel with Hyperkernels},
  author = {Ong, CS. and Smola, A. and Williamson, R.},
  journal = {Journal of Machine Learning Research},
  volume = {6},
  pages = {1043-1071},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = jul,
  year = {2005},
  month_numeric = {7}