1999 TransductiveInfForTextClassUsingSVM

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Subject Headings: Text Classification Algorithm, Transductive Support Vector Machine, Small Training Set, Small Training Set Learning Algorithm.

Notes

Cited By

~812 http://scholar.google.com/scholar?cites=978030212056845634

Quotes

Abstract

  • This paper introduces Transductive Support Vector Machines (TSVMs) for text classification. While regular Support Vector Machines (SVMs) try to induce a general decision function for a learning task, Transductive Support Vector Machines take into account a particular test set and try to minimize misclassifications of just those particular examples. The paper presents an analysis of why TSVMs are well suited for text classification. These theoretical findings are supported by experiments on three test collections. The experiments show substantial improvements over inductive methods, especially for small training sets, cutting the number of labeled training examples down to a twentieth on some tasks. This work also proposes an algorithm for training TSVMs efficiently, handling 10,000 examples and more. 1

References

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 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
1999 TransductiveInfForTextClassUsingSVMThorsten JoachimsTransductive Inference for Text Classification Using Support Vector MachinesProceedings of the Sixteenth International Conference on Machine Learninghttps://eprints.kfupm.edu.sa/73103/1/73103.pdf1999