Learning from Positive and Unlabeled Examples (2000)  (Make Corrections)  (4 citations)
Fabien Letouzey, François Denis, Rémi Gilleron
Algorithmic Learning Theory, 11th International Conference, ALT 2000, Sydney, Australia, December 2000, Proceedings

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Abstract: In many machine learning settings, examples of one class (called positive class) are easily available. Also, unlabeled data are abundant. (Update)

Context of citations to this paper:   More

.... Web page classification problem, unlabeled and positive data are widely available while negative data sets are rare and expensive [13, 5]. For example, consider the automatic diagnosis of diseases: unlabeled data are easy to collect (all patients in the database) and...

Cited by:   More
PEBL: Web Page Classification - Without Negative Examples   (Correct)
In Partial Fulfillment of the Requirements for the Degree of - Doctor Of Philosophy   (Correct)
Text Classication from Positive and Unlabeled Examples - Franois Denis Quipe   (Correct)

Active bibliography (related documents):   More   All
0.3:   PEBL: Positive Example Based Learning for Web Page Classification .. - Yu, Han (2002)   (Correct)
0.2:   Semi-supervised Learning of Classifiers: Theory, Algorithms - And Their Application   (Correct)
0.2:   Unknown - Copyright By Ira   (Correct)

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0.5:   Learning Regular Languages From Simple Positive Examples - Denis (2000)   (Correct)
0.4:   PAC Learning with Simple Examples - Denis, D'Halluin, Gilleron (1996)   (Correct)
0.3:   PAC Learning under Helpful Distributions - Denis, Gilleron (1997)   (Correct)

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4:   on Machine Learning (context) - th, Conf - 1993
4:   Advances in Neural Information Processing Systems (context) - Touretzky, Mozer et al. - 1997
3:   Positive and unlabeled examples help learning (context) - DeComite, Denis et al. - 1999

BibTeX entry:   (Update)

F. Letouzey, F. Denis, and R. Gilleton. Learning from positive and unlabeled examples. In ALT, 2000. http://citeseer.ist.psu.edu/letouzey00learning.html   More

@inproceedings{ letouzey00learning,
    author = "Fabien Letouzey and Fran{\c{c}}ois Denis and R{\'{e}}mi Gilleron",
    title = "Learning from Positive and Unlabeled Examples",
    booktitle = "Algorithmic Learning Theory, 11th International Conference, {ALT} 2000, Sydney, Australia, December 2000, Proceedings",
    volume = "1968",
    publisher = "Springer, Berlin",
    pages = "71--85",
    year = "2000",
    url = "citeseer.ist.psu.edu/letouzey00learning.html" }
Citations (may not include all citations):
1940   Programs for Machine Learning (context) - Quinlan - 1993
518   A theory of the learnable (context) - Valiant
235   An Introduction to Computational Learning Theory (context) - Kearns, Vazirani - 1994
146   Combining labeled and unlabeled data with cotraining - Blum, Mitchell - 1998
141   Learning from noisy examples (context) - Angluin, Laird - 1988
117   Classi cation and regression trees (context) - Breiman, Friedman et al. - 1984
78   Equivalence of models for polynomial learnability (context) - Haussler, Kearns et al. - 1991
21   The role of unlabeled data in supervised learning - Mitchell - 1999
18   Ecient noise-tolerant learning from statistical queries (context) - Kearns - 1993
9   and the statistical query model (context) - Blum, Kalai et al. - 2000
7   PAC learning from positive statistical queries - Denis - 1998
6   Positive and unlabeled examples help learning (context) - DeComit, Denis et al. - 1999
5   Pac learning with constant-partition classi cation noise and.. (context) - Decatur - 1997
1   the eciency of noise-tolerant pac algorithms derived from st.. (context) - Jackson - 2000

Documents on the same site (http://www.cmi.univ-mrs.fr/~fdenis/):   More
PAC Learning from Positive Statistical Queries - Denis (1998)   (Correct)
PAC Learning with Simple Examples - Denis, D'Halluin, Gilleron (1996)   (Correct)
Positive and Unlabelled Examples Help Learning - De Comit, Denis, Gilleron..   (Correct)

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