Abstract. We study the problem of learning from positive and unlabeled examples. Although several techniques exist for dealing with this problem, they all assume that positive exam...
Traditional classification involves building a classifier using labeled training examples from a set of predefined classes and then applying the classifier to classify test instan...
Abstract. The problem of classification from positive and unlabeled examples attracts much attention currently. However, when the number of unlabeled negative examples is very sma...
Xiaoling Wang, Zhen Xu, Chaofeng Sha, Martin Ester...
We present a discriminative method for learning selectional preferences from unlabeled text. Positive examples are taken from observed predicate-argument pairs, while negatives ar...
This paper presents a novel solution for the problem of building text classifier using positive documents (P) and unlabeled documents (U). Here, the unlabeled documents are mixed w...