BibSonomy publications for /user/jil/tutorialhttp://www.bibsonomy.org/burst/user/jil/tutorialBibSonomy BuRST Feed for /user/jil/tutorial2008-07-21T01:20:52+02:00- A Practical Guide to Support Vector Classificationhttp://www.bibsonomy.org/bibtex/2c04ef97dc3c3de168e684c3e4abe061b/jiljil2008-05-28T19:14:22+02:00libsvm svm guide tutorial <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 Classification2003libsvm svm guide tutorial 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.
- A Short SVM (Support Vector Machine) Tutorialhttp://www.bibsonomy.org/bibtex/2b7cf853e8635bd2887e8dea3d9e10ccb/jiljil2008-05-04T17:00:45+02:00tutorial svm mathematik kkt math mathe lagrange 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) Tutorial2004tutorial svm mathematik kkt math mathe lagrange background
- A Tutorial on Support Vector Machines for Pattern Recognitionhttp://www.bibsonomy.org/bibtex/2ad2a33b52e690eaf15da04fff7f12755/jiljil2008-05-04T16:52:21+02:00svm burges herleitung tutorial lagrange kkt deduction <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 Recognition21998svm burges herleitung tutorial lagrange kkt deduction
- A tutorial on \ν-support vector machines: Research ArticlesA tutorial on ν-support vector machineshttp://www.bibsonomy.org/bibtex/24849dc190c907bcb507aece582e76353/jiljil2008-05-02T00:49:56+02:00svm tutorial trick kernel <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\"{o}lkopf">Schölkopf</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 Articles212005svm tutorial trick kernel 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].