Accepted for NIPS ’ 99 SV Estimation of a Distribution ’ s Support
Suppose you are given some dataset drawn from an underlying p robability distributionP and you want to estimate a subset S of input space such that the probability that a test point drawn from P lies outside ofS is bounded by some a priori specified 0 < 1. We propose an algorithm which approaches this problem by try ing to estimate a functionf which is positive onS 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 regu larized by controlling the length of the weight vector in an associated fea ture space. The algorithm is a natural extension of the support vector al gorithm to the case of unlabelled data.