@inproceedings{joachims1999,
	title = {Transductive Inference for Text Classification using Support Vector Machines},
	address = {Bled, SL},
	author = {Thorsten Joachims},
	booktitle = {Proceedings of {ICML}-99, 16th International Conference on Machine Learning},
	editor = {Ivan Bratko and Saso Dzeroski},
	pages = {200--209},
	publisher = {Morgan Kaufmann Publishers, San Francisco, US},
	url = {http://www.joachims.org/publications/joachims_99c.ps.gz},
	year = {1999},
	biburl = {http://www.bibsonomy.org/bibtex/27cf3e7981cac898c1745418db83e0fd6/jil},
	abstract = {This paper introduces Transductive Support Vector Machines (TSVMs) for text classifi­ cation. While regular Support Vector Ma­ chines (SVMs) try to induce a general deci­ sion function for a learning task, Transduc­ tive Support Vector Machines take into ac­ count a particular test set and try to mini­ mize misclassifications of just those particu­ lar examples. The paper presents an anal­ ysis of why TSVMs are well suited for text classification. These theoretical findings are supported by experiments on three test col­ lections. The experiments show substantial improvements over inductive methods, espe­ cially for small training sets, cutting the num­ ber of labeled training examples down to a twentieth on some tasks. This work also pro­ poses an algorithm for training TSVMs effi­ ciently, handling 10,000 examples and more.},
	pdf = {joachims99.pdf}, lastname = {Joachims}, lastdatemodified = {2005-08-06}, read = {notread}, own = {own},
	keywords = {svm svmlight transductive }
}