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Analyzing the Effectiveness and Applicability of Co-training (2000)
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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...
Keyphrases
co-training algorithm co-training setting information search significant interest information storage unlabeled data natural split text learning task subject descriptor feature split embedded classifier retrieval information filtering keywords co-training natural independent split supervised learning algorithm natural separation disjoint set artificial intelligence
