http://www.bibsonomy.org/burst/user/jil/svmBibSonomy BuRST Feed for /user/jil/svm2008-07-21T01:27:00+02:00http://www.bibsonomy.org/bibtex/2c04ef97dc3c3de168e684c3e4abe061b/jiljil2008-05-28T19:14:22+02:00tutorial svm guide libsvm <span style="color:#555555;">Chih-Wei <a href="http://www.bibsonomy.org/author/Hsu">Hsu</a> and Chih-Chung <a href="http://www.bibsonomy.org/author/Chang">Chang</a> and Chih-Jen <a href="http://www.bibsonomy.org/author/Lin">Lin</a> </span><em>Department of Computer Science, National Taiwan University, </em>(<em>2003</em>)Wed May 28 19:14:22 CEST 2008A Practical Guide to Support Vector Classification2003tutorial svm guide libsvm Support vector machine (SVM) is a popular technique for classification. However, beginners who are not familiar with SVM often get unsatisfactory results since they miss some easy but significant steps. In this guide, we propose a simple procedure, which usually gives reasonable results.http://www.bibsonomy.org/bibtex/2b91671f0203ceba841281cf9daf523ea/jiljil2008-05-28T19:07:36+02:00svm svmlight light joachims 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 urlhttp://svmlight.joachims.org/ . </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 svm svmlight light joachims svm_light http://www.bibsonomy.org/bibtex/2b7cf853e8635bd2887e8dea3d9e10ccb/jiljil2008-05-04T17:00:45+02:00mathematik math svm lagrange kkt tutorial mathe background <span style="color:#555555;">J.P. <a href="http://www.bibsonomy.org/author/Lewis">Lewis</a> </span><em>CGIT Lab / IMSC, </em>(<em>2004</em>)Sun May 04 17:00:45 CEST 2008A Short SVM (Support Vector Machine) Tutorial2004mathematik math svm lagrange kkt tutorial mathe background http://www.bibsonomy.org/bibtex/2ad2a33b52e690eaf15da04fff7f12755/jiljil2008-05-04T16:52:21+02:00tutorial burges svm deduction herleitung lagrange kkt <span style="color:#555555;">Christopher J. C. <a href="http://www.bibsonomy.org/author/Burges">Burges</a> </span><em>Data Mining and Knowledge Discovery</em><em>2(2):121-167</em>(<em>1998</em>)Sun May 04 16:52:21 CEST 2008Data Mining and Knowledge Discovery2121-167A Tutorial on Support Vector Machines for Pattern Recognition21998tutorial burges svm deduction herleitung lagrange kkt http://www.bibsonomy.org/bibtex/27cf3e7981cac898c1745418db83e0fd6/jiljil2008-05-02T15:34:36+02:00svm transductive svmlight <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>page200--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 Machines1999svm transductive svmlight 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.http://www.bibsonomy.org/bibtex/2b855be60a3903e0af45ca42fd20c2732/jiljil2008-05-02T00:54:16+02:00optimization svm reformulation optimierung linear <span style="color:#555555;">Thorsten <a href="http://www.bibsonomy.org/author/Joachims">Joachims</a> </span><em>KDD, </em><em>page217-226. </em><em>ACM, </em>(<em>2006</em>)Fri May 02 00:54:16 CEST 2008KDDconf/kdd/2006217-226Training linear SVMs in linear time.2006optimization svm reformulation optimierung linear A tutorial on ν-support vector machineshttp://www.bibsonomy.org/bibtex/24849dc190c907bcb507aece582e76353/jiljil2008-05-02T00:49:56+02:00tutorial kernel svm trick <span style="color:#555555;">Pai-Hsuen <a href="http://www.bibsonomy.org/author/Chen">Chen</a> and Chih-Jen <a href="http://www.bibsonomy.org/author/Lin">Lin</a> and Bernhard <a href="http://www.bibsonomy.org/author/Sch\&#034;{o}lkopf">Sch&#246;lkopf</a> </span><em>Appl. Stoch. Model. Bus. Ind.</em><em>21(2):111--136</em>(<em>2005</em>)Fri May 02 00:49:56 CEST 2008Chichester, UK, UKAppl. Stoch. Model. Bus. Ind.2111--136A tutorial on \ν-support vector machines: Research Articles212005tutorial kernel svm trick We briefly describe the main ideas of statistical learning theory, support vector machines (SVMs), and kernel feature spaces. We place particular emphasis on a description of the so-called ν-SVM, including details of the algorithm and its implementation, theoretical results, and practical applications. Copyright © 2005 John Wiley & Sons, Ltd.Parts of the present article are based on [1].http://www.bibsonomy.org/bibtex/28b298c325c6ecdb9c01e01057464ae2d/jiljil2008-03-29T06:27:14+01:00classification svm multilabel hotho <span style="color:#555555;">Boris <a href="http://www.bibsonomy.org/author/Lauser">Lauser</a> and Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> </span><em>Proc. of the 7th European Conference in Research and Advanced Technology for Digital Libraries, ECDL 2003, </em><em>volume2769ofLNCS, </em><em>page140-151. </em><em>Springer, </em>(<em>2003</em>)Sat Mar 29 06:27:14 CET 2008Proc. of the 7th European Conference in Research and Advanced Technology for Digital Libraries, ECDL 2003140-151LNCSAutomatic multi-label subject indexing in a multilingual environment27692003classification svm multilabel hotho http://www.bibsonomy.org/bibtex/25206a28e84c94a9226969a8232d37c48/jiljil2008-01-21T17:59:17+01:00svm j optimierung parameter <span style="color:#555555;">David D. <a href="http://www.bibsonomy.org/author/Lewis">Lewis</a> </span><em>TREC, </em>(<em>2001</em>)Mon Jan 21 17:59:17 CET 2008TRECApplying Support Vector Machines to the TREC-2001 Batch Filtering and Routing Tasks.2001svm j optimierung parameter http://www.bibsonomy.org/bibtex/21946b7254ef1dccdf34c6140ad133c67/jiljil2008-01-21T17:31:57+01:00machine text parameter j svm learning optimierung <span style="color:#555555;">Mikhail S. <a href="http://www.bibsonomy.org/author/Ageev">Ageev</a> and Boris V. <a href="http://www.bibsonomy.org/author/Dobrov">Dobrov</a> </span><em>ISTA, </em><em>volume30ofLNI, </em><em>page165-176. </em><em>GI, </em>(<em>2003</em>)Mon Jan 21 17:31:57 CET 2008ISTAconf/ista/2003165-176LNISupport Vector Machine Parameter Optimization for Text Categorization Problems.302003machine text parameter j svm learning optimierung