@inproceedings{0afe333721f445b59f9cf60bd3504048,
title = "Support vector method for novelty detection",
abstract = "Suppose you are given some dataset drawn from an underlying probability distribution P and you want to estimate a {"}simple{"} subset S of input space such that the probability that a test point drawn from P lies outside of S equals some a priori specified ν between 0 and 1. We propose a method to approach this problem by trying to estimate a function / which is positive on S 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.",
author = "Bernhard Sch{\"o}lkopf and Robert Williamson and Alex Smola and John Shawe-Taylor and John Piatt",
year = "2000",
language = "English",
isbn = "0262194503",
series = "Advances in Neural Information Processing Systems",
publisher = "Neural Information Processing Systems Foundation",
pages = "582--588",
booktitle = "Advances in Neural Information Processing Systems 12 - Proceedings of the 1999 Conference, NIPS 1999",
note = "13th Annual Neural Information Processing Systems Conference, NIPS 1999 ; Conference date: 29-11-1999 Through 04-12-1999",
}