Transductive Inference for Text Classification using Support Vector Machines
ICML(1999)
摘要
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, TSVMs 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 20th on some
tasks. This work also proposes an algorithm for training
TSVMs efficiently, handling 10,000 examples and more.
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关键词
support vector machines,transductive inference,text classification,support vector machine
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