A New PU Learning Algorithm for Text Classification

This paper studies the problem of building text classifiers using positive and unlabeled examples. The primary challenge of this problem as compared with classical text classification problem is that no labeled negative documents are available in the training example set. We call this problem PU-Ori...

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Bibliographische Detailangaben

Hauptverfasser:	Yu, Hailong, Zuo, Wanli, Peng, Tao
Format:	Buchkapitel
Sprache:	eng
Schlagworte:	Final Classifier Negative Data Probably Approximately Correct Unlabeled Data Weighted Vote
Online-Zugang:	Volltext
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