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Transductive Inference for Text Classification using Support Vector Machines (1999)
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BibTeX
@INPROCEEDINGS{Joachims99transductiveinference,
author = {Thorsten Joachims},
title = {Transductive Inference for Text Classification using Support Vector Machines},
booktitle = {},
year = {1999},
pages = {200--209},
publisher = {Morgan Kaufmann}
}
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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.
Keyphrases
text classification support vector machine transductive inference transductive support vector machine general decision function particular test set theoretical finding inductive method labeled training example regular support vector machine small training set particular example substantial improvement learning task test collection
