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.

Citations

8174	The Nature of Statistical Learning Theory - Vapnik - 1995
1699	An algorithm for suffix stripping - Porter - 1980
1581	Text categorization with support vector machines: Learning with many relevant features - Joachims - 1998
1429	Term-weighting approaches in automatic text retrieval - Salton, Buckley - 1988
1328	Making large-Scale SVM Learning Practical - Joachims - 1999
1160	Combining labeled and unlabeled data with cotraining - Blum, Mitchell - 1998
886	A comparative study on feature selection in text categorization - Yang, Pedersen - 1997
711	A comparison of event models for naive Bayes text classification - McCallum, Nigam - 1998
474	Inductive learning algorithms and representations for text categorization - Dumais, Platt, et al. - 1998
326	A probabilistic analysis of the Rocchio algorithm with TFIDF for text categorization - Joachims - 1997
154	Learning to classify text from labeled and unlabeled documents - Nigam, McCallum, et al. - 1998
122	A theoretical basis for the use of co-occurrence data in information retrieval - Rijsbergen - 1977
73	A Critical Investigation of Recall and Precision as Measures of Retrieval System Performance - Raghavan, Bollmann, et al. - 1989
67	Learning by transduction - Gammerman, Vapnik, et al. - 1998
66	Feature subset selection in text learning - Mladenić - 1998
55	Combining support vector and mathematical programming methods for induction - Bennett - 1999
35	An algorithm for su x stripping - Porter - 1980
11	Theorie der Zeichenerkennung - Wapnik, Tscherwonenkis - 1979
10	On structural risk minimization or overall risk in a problem of pattern recognition - Vapnik, Sterin - 1977
1	On structural risk minimization oroverall risk in a problem of pattern recognition. Automation and Remote Control - Vapnik, Sterin - 1977