%0 Conference Paper %1 joachims1999transductive %A Joachims, Thorsten %B Proceedings of ICML-99, 16th International Conference on Machine Learning %C Bled, SL %D 1999 %E Bratko, Ivan %E Dzeroski, Saso %I Morgan Kaufmann Publishers, San Francisco, US %K CAT CAT-SVM CAT-feature-selection %P 200--209 %T Transductive Inference for Text Classification using Support Vector Machines %U /brokenurl#joachims99.ps %X 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.

@inproceedings{joachims1999transductive, 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.}, added-at = {2011-12-12T21:09:48.000+0100}, address = {Bled, SL}, author = {Joachims, Thorsten}, biburl = {https://www.bibsonomy.org/bibtex/27cf3e7981cac898c1745418db83e0fd6/huiyangsfsu}, booktitle = {Proceedings of {ICML}-99, 16th International Conference on Machine Learning}, editor = {Bratko, Ivan and Dzeroski, Saso}, file = {joachims1999transductive.pdf:joachims1999transductive.pdf:PDF}, interhash = {7b7737625402ab3e8db5f53d1de716d6}, intrahash = {7cf3e7981cac898c1745418db83e0fd6}, keywords = {CAT CAT-SVM CAT-feature-selection}, lastdatemodified = {2005-08-06}, lastname = {Joachims}, own = {own}, pages = {200--209}, pdf = {joachims99.pdf}, publisher = {Morgan Kaufmann Publishers, San Francisco, US}, read = {notread}, timestamp = {2011-12-12T21:09:48.000+0100}, title = {Transductive Inference for Text Classification using Support Vector Machines}, url = {/brokenurl#joachims99.ps}, year = 1999 }