(Enter summary)
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
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In Partial Fulfillment of the Requirements for the Degree of - Doctor Of Philosophy
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Text Classication from Positive and Unlabeled Examples - Franois Denis Quipe
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0.3: PEBL: Positive Example Based Learning for Web Page Classification .. - Yu, Han (2002)
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0.2: Semi-supervised Learning of Classifiers: Theory, Algorithms - And Their Application
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0.2: Unknown - Copyright By Ira
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0.5: Learning Regular Languages From Simple Positive Examples - Denis (2000)
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0.4: PAC Learning with Simple Examples - Denis, D'Halluin, Gilleron (1996)
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0.3: PAC Learning under Helpful Distributions - Denis, Gilleron (1997)
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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):
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An Introduction to Computational Learning Theory (context) - Kearns, Vazirani - 1994
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Combining labeled and unlabeled data with cotraining
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and the statistical query model (context) - Blum, Kalai et al. - 2000
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PAC learning from positive statistical queries
- Denis - 1998
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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)
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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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