354 search results - page 1 / 71 » Learning classifiers from only positive and unlabeled data |
KDD
2008
ACM
14 years 4 months ago
2008
ACM
The input to an algorithm that learns a binary classifier normally consists of two sets of examples, where one set consists of positive examples of the concept to be learned, and ...
BMCBI
2010
13 years 4 months ago
2010
Background: Recently, supervised learning methods have been exploited to reconstruct gene regulatory networks from gene expression data. The reconstruction of a network is modeled...
WAIM
2010
Springer
13 years 9 months 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...
ECML
2007
Springer
13 years 10 months ago
2007
Springer
Many real-world classification applications fall into the class of positive and unlabeled (PU) learning problems. In many such applications, not only could the negative training ex...
IJCAI
2003
13 years 5 months ago
2003
In traditional text classification, a classifier is built using labeled training documents of every class. This paper studies a different problem. Given a set P of documents of a ...