Learning with Positive and Unlabeled Examples Using Weighted Logistic Regression

Learning with Positive and Unlabeled Examples Using Weighted Logistic Regression (2003)

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by Wee Sun Lee , Bing Liu

Venue:	Proceedings of the Twentieth International Conference on Machine Learning (ICML
Citations:	20 - 6 self

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@INPROCEEDINGS{Lee03learningwith,
    author = {Wee Sun Lee and Bing Liu},
    title = {Learning with Positive and Unlabeled Examples Using Weighted Logistic Regression},
    booktitle = {Proceedings of the Twentieth International Conference on Machine Learning (ICML},
    year = {2003},
    pages = {2003}
}

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Abstract

The problem of learning with positive and unlabeled examples arises frequently in retrieval applications.

Citations

2034	LIBSVM: A Library for Support Vector Machines,” Software available at http://www.csie.ntu.edu.tw/ ∼cjlin/libsvm - Chang, Lin - 2001
1086	Making large-scale SVM learning practical - Joachims - 1999
946	Combining labeled and unlabeled data with co-training - Blum, Mitchell - 1998
509	Transductive inference for text classification using support vector machines - Joachims - 1999
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358	TM: Machine Learning - Mitchell - 1997
353	Newsweeder: Learning to filter netnews - Lang - 1995
248	Efficient noise-tolerant learning from statistical queries - Kearns - 1998
140	Learning to classify text from labeled and unlabeled documents - Nigam, McCallum, et al. - 1998
85	Learning from positive data - Muggleton - 1314
77	Partially supervised classification of text documents - Liu, Lee, et al. - 2002
75	Robust trainability of single neurons - Höffgen, Simon - 1992
51	A Polynomial-time Algorithm for Learn-ing Noisy Linear Threshold Functions - Blum, Frieze, et al. - 1996
36	M.: Boosting algorithms as gradient descent in function space - Mason, Baxter, et al. - 1999
31	learning from positive statistical queries - PAC - 1998
27	R.: Athena: Mining-based interactive management of text databases - Agrawal, Bayardo, et al. - 2000
27	Learning linear threshold functions in the presence of classification noise - Bylander - 1994
24	Text classification from positive and unlabeled examples. The - Denis, Gilleron, et al. - 2002
6	One class SVMs for document classification - Manevitz, Yousef - 2001