@inproceedings{815,
  title = {Support vector method for novelty detection},
  journal = {Advances in Neural Information Processing Systems},
  booktitle = {Advances in Neural Information Processing Systems 12},
  abstract = {Suppose you are given some dataset drawn from an underlying probability distribution ¤ and you want to estimate a “simple” subset ¥ of input space such that the probability that a test point drawn from ¤ lies outside of ¥ equals some a priori specified ¦ between § and ¨. We propose a method to approach this problem by trying to estimate a function © which is positive on ¥ and negative on the complement. The functional form of © is given by a kernel expansion in terms of a potentially small subset of the training data; it is regularized by controlling the length of the weight vector in an associated feature space. We provide a theoretical analysis of the statistical performance of our algorithm. The algorithm is a natural extension of the support vector algorithm to the case of unlabelled data. },
  pages = {582-588},
  editors = {SA Solla and TK Leen and K-R M{\"u}ller},
  publisher = {MIT Press},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Cambridge, MA, USA},
  month = jun,
  year = {2000},
  slug = {815},
  author = {Sch{\"o}lkopf, B. and Williamson, RC. and Smola, AJ. and Shawe-Taylor, J. and Platt, JC.},
  month_numeric = {6}
}