@inproceedings{5155,
  title = {Tailoring density estimation via reproducing kernel moment matching},
  booktitle = {Proceedings of the 25th International Conference onMachine Learning},
  abstract = {Moment matching is a popular means of parametric
  density estimation. We extend this technique
  to nonparametric estimation of mixture
  models. Our approach works by embedding
  distributions into a reproducing kernel Hilbert
  space, and performing moment matching in that
  space. This allows us to tailor density estimators
  to a function class of interest (i.e., for which
  we would like to compute expectations). We
  show our density estimation approach is useful
  in applications such as message compression in
  graphical models, and image classification and
  retrieval.},
  pages = {992-999},
  editors = {WW Cohen and A McCallum and S Roweis},
  publisher = {ACM Press},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {New York, NY, USA},
  month = jul,
  year = {2008},
  slug = {5155},
  author = {Song, L. and Zhang, X. and Smola, A. and Gretton, A. and Sch{\"o}lkopf, B.},
  month_numeric = {7}
}