BibSonomy publications for /user/jil/svmlighthttp://www.bibsonomy.org/burst/user/jil/svmlightBibSonomy BuRST Feed for /user/jil/svmlight2008-10-12T01:25:30+02:00Making large-Scale SVM Learning Practical.http://www.bibsonomy.org/bibtex/2b91671f0203ceba841281cf9daf523ea/jiljil2008-05-28T19:07:36+02:00joachims svmlight svm light svm_light <span style="color:#555555;">T. <a href="http://www.bibsonomy.org/author/Joachims">Joachims</a> </span><em>Advances in Kernel Methods - Support Vector Learning, </em><em>MIT Press, </em>(<em>1999</em>) <em>software available at http://svmlight.joachims.org/
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</em>Wed May 28 19:07:36 CEST 2008 Advances in Kernel Methods - Support Vector Learning
software available at \url{http://svmlight.joachims.org/}Making large-Scale SVM Learning Practical. 1999 joachims svmlight svm light svm_light Transductive Inference for Text Classification using Support Vector Machineshttp://www.bibsonomy.org/bibtex/27cf3e7981cac898c1745418db83e0fd6/jiljil2008-05-02T15:34:36+02:00svmlight svm transductive <span style="color:#555555;">Thorsten <a href="http://www.bibsonomy.org/author/Joachims">Joachims</a> </span><em>Proceedings of ICML-99, 16th International Conference on Machine Learning, </em><em>Seite200--209. </em><em>Bled, SL, </em><em>Morgan Kaufmann Publishers, San Francisco, US, </em>(<em>1999</em>)Fri May 02 15:34:36 CEST 2008Bled, SLProceedings of {ICML}-99, 16th International Conference on Machine Learning200--209Transductive Inference for Text Classification using Support Vector Machines1999svmlight svm transductive 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.