7 search results - page 1 / 2 » Learning with Positive and Unlabeled Examples Using Weighted... |
ICML
2003
IEEE
14 years 8 months ago
2003
IEEE
The problem of learning with positive and unlabeled examples arises frequently in retrieval applications. We transform the problem into a problem of learning with noise by labelin...
WAIM
2010
Springer
14 years 16 days ago
2010
Springer
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...
CEAS
2006
Springer
13 years 11 months ago
2006
Springer
Most spam filters are configured for use at a very low falsepositive rate. Typically, the filters are trained with techniques that optimize accuracy or entropy, rather than perfor...
SIGIR
2008
ACM
13 years 7 months ago
2008
ACM
It is difficult to apply machine learning to new domains because often we lack labeled problem instances. In this paper, we provide a solution to this problem that leverages domai...
UAI
2003
13 years 8 months ago
2003
We formulate a principle for classification with the knowledge of the marginal distribution over the data points (unlabeled data). The principle is cast in terms of Tikhonov styl...