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» Text classification from positive and unlabeled documents
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WWW
2004
ACM
14 years 9 months ago
Dealing with different distributions in learning from
In the problem of learning with positive and unlabeled examples, existing research all assumes that positive examples P and the hidden positive examples in the unlabeled set U are...
Xiaoli Li, Bing Liu
ECIR
2009
Springer
13 years 6 months ago
Active Learning Strategies for Multi-Label Text Classification
Abstract. Active learning refers to the task of devising a ranking function that, given a classifier trained from relatively few training examples, ranks a set of additional unlabe...
Andrea Esuli, Fabrizio Sebastiani
ECML
2005
Springer
14 years 2 months ago
Learning from Positive and Unlabeled Examples with Different Data Distributions
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...
Xiaoli Li, Bing Liu
KDD
2008
ACM
137views Data Mining» more  KDD 2008»
14 years 9 months ago
Learning classifiers from only positive and unlabeled data
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 ...
Charles Elkan, Keith Noto
BMCBI
2010
143views more  BMCBI 2010»
13 years 8 months ago
Learning gene regulatory networks from only positive and unlabeled data
Background: Recently, supervised learning methods have been exploited to reconstruct gene regulatory networks from gene expression data. The reconstruction of a network is modeled...
Luigi Cerulo, Charles Elkan, Michele Ceccarelli