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Semi-supervised Collaborative Text Classification.
- Source :
- Machine Learning: ECML 2007; 2007, p600-607, 8p
- Publication Year :
- 2007
-
Abstract
- Most text categorization methods require text content of documents that is often difficult to obtain. We consider "Collaborative Text Categorization", where each document is represented by the feedback from a large number of users. Our study focuses on the semi-supervised case in which one key challenge is that a significant number of users have not rated any labeled document. To address this problem, we examine several semi-supervised learning methods and our empirical study shows that collaborative text categorization is more effective than content-based text categorization and the manifold regularization is more effective than other state-of-the-art semi-supervised learning methods. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISBNs :
- 9783540749578
- Database :
- Complementary Index
- Journal :
- Machine Learning: ECML 2007
- Publication Type :
- Book
- Accession number :
- 33170069
- Full Text :
- https://doi.org/10.1007/978-3-540-74958-5_58