A survey of recent trends in one class classification <s> Availability of Training Data <s> 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 f which is positive on S and negative on the complement. The functional form of f 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. <s> BIB001 </s> A survey of recent trends in one class classification <s> Availability of Training Data <s> We study the problem of building text classifiers using positive and unlabeled examples. The key feature of this problem is that there is no negative example for learning. Recently, a few techniques for solving this problem were proposed in the literature. These techniques are based on the same idea, which builds a classifier in two steps. Each existing technique uses a different method for each step. We first introduce some new methods for the two steps, and perform a comprehensive evaluation of all possible combinations of methods of the two steps. We then propose a more principled approach to solving the problem based on a biased formulation of SVM, and show experimentally that it is more accurate than the existing techniques. <s> BIB002
OCC problems have been studied extensively under three broad frameworks: The last category has recieved much research interest among the text/document classification community BIB002 [9][10] that will be discussed in detail below in Section 3.3.  [12] and Scholkopf et al. BIB001 have developed algorithms based on support vector machines to tackle the problem of OCC using positive examples only (refer to Section 3.2). The main idea behind these strategies is to construct a decision boundary around the positive data so as to differentiate the outliers (non-positives) from the positive data.