Help Privacy Policy Disclaimer
  Advanced SearchBrowse




Conference Paper

Support vector method for novelty detection

There are no MPG-Authors in the publication available
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available

Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., & Platt, J. (2000). Support vector method for novelty detection. In S. Solla, T. Leen, & K. Müller (Eds.), Advances in Neural Information Processing Systems 12 (pp. 582-588). Cambridge, MA, USA: MIT Press.

Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-E4C8-3
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.