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Analyzing the Effectiveness and Applicability of Co-training (2000)

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by Kamal Nigam , Rayid Ghani
Citations:157 - 7 self
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@INPROCEEDINGS{Nigam00analyzingthe,
    author = {Kamal Nigam and Rayid Ghani},
    title = {Analyzing the Effectiveness and Applicability of Co-training},
    booktitle = {},
    year = {2000},
    pages = {86--93}
}

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Abstract

Recently there has been significant interest in supervised learning algorithms that combine labeled and unlabeled data for text learning tasks. The co-training setting [1] applies to datasets that have a natural separation of their features into two disjoint sets. We demonstrate that when learning from labeled and unlabeled data, algorithms explicitly leveraging a natural independent split of the features outperform algorithms that do not. When a natural split does not exist, co-training algorithms that manufacture a feature split may out-perform algorithms not using a split. These results help explain why co-training algorithms are both discriminative in nature and robust to the assumptions of their embedded classifiers. Categories and Subject Descriptors I.2.6 [Artificial Intelligence]: Learning; H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval--- Information Filtering Keywords co-training, expectation-maximization, learning with labeled and unlabeled...

Citations

6232 Maximum likelihood from incomplete data via the EM algorithm - Dempster, Laird, et al. - 1977
946 Combining labeled and unlabeled data with co-training - Blum, Mitchell - 1998
632 Text classification from labeled and unlabeled documents using - Nigram, McCallum, et al.
619 Nigam K: A comparison of event models for naïve Bayes text classification - McCallum - 1998
509 Transductive inference for text classification using support vector machines - Joachims - 1999
486 Pazzani M: On the optimality of the simple Bayesian classifier under zero-one loss - Domingos - 1997
402 Active learning with statistical models - Cohn, Ghahramani, et al. - 1996
383 Unsupervised Word Sense Disambiguation Rivaling Supervised Methods - Yarowsky - 1995
359 Unsupervised models for named entity classification - Collins, Singer - 1999
315 Exploiting generative models in discriminative classifiers - Jaakkola, Haussler - 1998
285 A probabilistic analysis of the rocchio algorithm with tfidf for text categorization - Joachims - 1997
268 Naive (Bayes) at forty: The independence assumption in information retrieval - Lewis - 1998
198 Employing EM and pool-based active learning for text classification - McCallum, Nigam - 1998
151 Using probabilistic models of document retrieval without relevance information - Croft, Harper - 1979
122 New retrieval approaches using smart: Trec 4 - Buckley, Amit - 1995
55 Relational learning with statistical predicate invention: Better models for hypertext - Craven, Slattery - 2001
22 Algorithms for graph partitioning: A survey”, Linkoping - Fjallstrom
20 Learning dictionaries for information extraction using multi-level bootstrapping - Riloff - 1999
11 Text classi¯ cation fromlabeled and unlabeled documents using em - Nigam, McCallum, et al. - 2011
7 Transductive inference for text classi cation using support vector machines - Joachims - 1999
3 Learning dictionaries for information extraction using multi-level boot-strapping - Rilo, Jones - 1999
1 On the optimality ofthe simple Bayesian classi er under zero-one loss - Domingos, Pazzani - 1997
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