1999 TransductiveInferenceForTextClass
- (Joachims, 1999b) ⇒ Thorsten Joachims. (1999). “Transductive Inference for Text Classification using Support Vector Machines.” In: Proceedings of the International Conference on Machine Learning (ICML 1999).
Subject Headings: Transductive Learning Algorithm, SVMlight.
Notes
- It has won a 10-year paper award.
Cited By
2002
- (Sebastiani, 2002) ⇒ Fabrizio Sebastiani. (2002). “Machine Learning in Automated Text Categorization.” In: Association of Computing Machinery Computing Surveys, 34(1). doi:10.1145/505282.505283.
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.
References
,
| Author | volume | Date Value | title | type | journal | titleUrl | doi | note | year | |
|---|---|---|---|---|---|---|---|---|---|---|
| 1999 TransductiveInferenceForTextClass | Thorsten Joachims | Transductive Inference for Text Classification using Support Vector Machines | Proceedings of the International Conference on Machine Learning | http://www.cs.cornell.edu/People/tj/publications/joachims 99c.pdf | 1999 |