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Abstract: Recently there has been significantinterest 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... (Update)
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BibTeX entry: (Update)
Kamal Nigam and Rayid Ghani. 2000. Analyzing the effectiveness and applicability of co-training. In Proc. of Ninth International Conference on Information and Knowledge (CIKM-2000). http://citeseer.ist.psu.edu/nigam00analyzing.html More
@inproceedings{ nigam00analyzing,
author = "Kamal Nigam and Rayid Ghani",
title = "Analyzing the Effectiveness and Applicability of Co-training",
booktitle = "{CIKM}",
pages = "86-93",
year = "2000",
url = "citeseer.ist.psu.edu/nigam00analyzing.html" }
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