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Transductive Inference for Text Classification using Support Vector Machines (1999)

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by Thorsten Joachims
Citations:877 - 4 self
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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   

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