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Document representation for one-class SVM. (English) Zbl 1132.68609

Boulicaut, J.-F. (ed.) et al., Machine learning: ECML 2004. 15th European conference on machine learning, Pisa, Italy, September 20–24, 2004, Proceedings. Berlin: Springer (ISBN 978-3-540-23105-9/pbk). Lecture Notes in Computer Science 3201. Lecture Notes in Artificial Intelligence, 489-500 (2004).
Summary: Previous studies have shown that one-class SVM is a rather weak learning method for text categorization problems. This paper points out that the poor performance observed before is largely due to the fact that the standard term weighting schemes are inadequate for one-class SVMs. We propose several representation modifications, and demonstrate empirically that, with the proposed document representation, the performance of one-class SVM, although trained on only small portion of positive examples, can reach up to 95% of that of two-class SVM trained on the whole labeled dataset.
For the entire collection see [Zbl 1131.68005].

MSC:

68T05 Learning and adaptive systems in artificial intelligence
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