Analyzing the Effectiveness and Applicability of Co-training

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

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by Kamal Nigam , Rayid Ghani

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Datum	Value	Source
TITLE	Analyzing the Effectiveness and Applicability of Co-training	user correction - Legacy Corrections
AUTHOR NAME	Kamal Nigam	SVM HeaderParse 0.1
AUTHOR AFFIL	School of Computer Science; Carnegie Mellon University	SVM HeaderParse 0.2
AUTHOR ADDR	Pittsburgh, PA 15213	SVM HeaderParse 0.1
AUTHOR NAME	Rayid Ghani	SVM HeaderParse 0.1
AUTHOR AFFIL	School of Computer Science; Carnegie Mellon University	SVM HeaderParse 0.2
AUTHOR ADDR	Pittsburgh, PA 15213	SVM HeaderParse 0.1
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...	user correction - Legacy Corrections
YEAR	2000	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	86--93	INFERENCE
CITATIONS	18 found	ParsCit 1.0