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» Learning to Classify Texts Using Positive and Unlabeled Data
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IJCAI
2003
9 years 5 months ago
Learning to Classify Texts Using Positive and Unlabeled Data
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 ...
Xiaoli Li, Bing Liu
AMT
2006
Springer
147views Multimedia» more  AMT 2006»
9 years 7 months ago
Semi-Supervised Text Classification Using Positive and Unlabeled Data
Text classification using positive and unlabeled data refers to the problem of building text classifier using positive documents (P) of one class and unlabeled documents (U) of man...
Shuang Yu, Xueyuan Zhou, Chunping Li
KDD
2008
ACM
137views Data Mining» more  KDD 2008»
10 years 4 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
ICDM
2003
IEEE
128views Data Mining» more  ICDM 2003»
9 years 9 months ago
Building Text Classifiers Using Positive and Unlabeled Examples
Bing Liu, Yang Dai, Xiaoli Li, Wee Sun Lee, Philip...
AAAI
1998
9 years 5 months ago
Learning to Classify Text from Labeled and Unlabeled Documents
In many important text classification problems, acquiring class labels for training documents is costly, while gathering large quantities of unlabeled data is cheap. This paper sh...
Kamal Nigam, Andrew McCallum, Sebastian Thrun, Tom...
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