@inproceedings{2261,
  title = {Distance-based classification with Lipschitz functions},
  journal = {Learning Theory and Kernel Machines, Proceedings of the 16th Annual Conference on Computational Learning Theory},
  abstract = {The goal of this article is to develop a framework for large margin
  classification in metric spaces.  We want to find a generalization of
  linear decision functions for metric spaces and define a corresponding
  notion of margin such that the decision function separates the
  training points with a large margin. It will turn out that using
  Lipschitz functions as decision functions, the inverse of the Lipschitz
  constant can be interpreted as the size of a margin. In order to
  construct a clean mathematical setup we isometrically embed the given
  metric space into a Banach space and the space of Lipschitz functions
  into its dual space.  Our approach leads to a general large margin
  algorithm for classification in metric spaces. To analyze this
  algorithm, we first prove a representer theorem. It states that there
  exists a solution which can be expressed as linear combination of
  distances to sets of training points. Then we analyze the Rademacher
  complexity of some Lipschitz function classes. The generality of the
  Lipschitz approach can be seen from the fact that several well-known
  algorithms are special cases of the Lipschitz algorithm, among them
  the support vector machine, the linear programming machine, and
  the 1-nearest neighbor classifier.},
  pages = {314-328},
  editors = {Sch{\"o}lkopf, B. and M.K. Warmuth},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  year = {2003},
  author = {von Luxburg, U. and Bousquet, O.}
}