1999 TransductiveInfForTextClassUsingSVM

• (Joachims, 1999) ⇒ Thorsten Joachims. (1999). “Transductive Inference for Text Classification Using Support Vector Machines.” In: Proceedings of the Sixteenth International Conference on Machine Learning (ICML 1999).

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

Cited By

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

• (Bilenko and Mooney, 2003) ⇒ Mikhail Bilenko, and Raymond Mooney. (2003). “Adaptive Duplicate Detection Using Learnable String Similarity Measures.” In: Proceedings of the ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2003).

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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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
1999 TransductiveInfForTextClassUsingSVM	Thorsten Joachims			Transductive Inference for Text Classification Using Support Vector Machines			https://eprints.kfupm.edu.sa/73103/1/73103.pdf