@techreport{libsvmTutorial, title = {A Practical Guide to Support Vector Classification}, author = {Chih-Wei Hsu and Chih-Chung Chang and Chih-Jen Lin}, institution = {Department of Computer Science, National Taiwan University}, year = 2003, url = {http://www.csie.ntu.edu.tw/~cjlin/papers.html}, abstract = {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.}, biburl = {http://www.bibsonomy.org/bibtex/2c04ef97dc3c3de168e684c3e4abe061b/jil}, keywords = {libsvm tutorial svm guide} } @inproceedings{svmlight, title = {Making large-Scale SVM Learning Practical. }, author = {T. Joachims}, booktitle = { Advances in Kernel Methods - Support Vector Learning }, note = {software available at \url{http://svmlight.joachims.org/}}, publisher = { MIT Press }, year = { 1999 }, biburl = {http://www.bibsonomy.org/bibtex/2b91671f0203ceba841281cf9daf523ea/jil}, keywords = {svm_light svm light joachims svmlight} } @techreport{lewis2004tutorial, title = {A Short SVM (Support Vector Machine) Tutorial}, author = {J.P. Lewis}, institution = {CGIT Lab / IMSC}, year = 2004, url = {http://www.idiom.com/~zilla/Work/Notes/svmtutorial.pdf}, biburl = {http://www.bibsonomy.org/bibtex/2b7cf853e8635bd2887e8dea3d9e10ccb/jil}, keywords = {tutorial mathe svm kkt math mathematik background lagrange} } @article{burges1998, title = {A Tutorial on Support Vector Machines for Pattern Recognition}, author = {Christopher J. C. Burges}, journal = {Data Mining and Knowledge Discovery}, number = 2, pages = {121-167}, volume = 2, year = 1998, url = {citeseer.ist.psu.edu/burges98tutorial.html}, biburl = {http://www.bibsonomy.org/bibtex/2ad2a33b52e690eaf15da04fff7f12755/jil}, keywords = {lagrange herleitung deduction svm burges kkt tutorial} } @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}, year = 1999, url = {http://www.joachims.org/publications/joachims_99c.ps.gz}, lastdatemodified = {2005-08-06}, pdf = {joachims99.pdf}, read = {notread}, lastname = {Joachims}, own = {own}, 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.}, biburl = {http://www.bibsonomy.org/bibtex/27cf3e7981cac898c1745418db83e0fd6/jil}, keywords = {svmlight transductive svm} } @inproceedings{joachims2006a, title = {Training linear SVMs in linear time.}, author = {Thorsten Joachims}, booktitle = {KDD}, crossref = {conf/kdd/2006}, editor = {Tina Eliassi-Rad and Lyle H. Ungar and Mark Craven and Dimitrios Gunopulos}, pages = {217-226}, publisher = {ACM}, year = 2006, url = {http://dblp.uni-trier.de/db/conf/kdd/kdd2006.html#Joachims06}, ee = {http://doi.acm.org/10.1145/1150402.1150429}, isbn = {1-59593-339-5}, date = {2006-10-05}, biburl = {http://www.bibsonomy.org/bibtex/2b855be60a3903e0af45ca42fd20c2732/jil}, keywords = {svm optimization optimierung reformulation linear} } @article{vsvm, title = {A tutorial on \ν-support vector machines: Research Articles}, address = {Chichester, UK, UK}, author = {Pai-Hsuen Chen and Chih-Jen Lin and Bernhard Sch\"{o}lkopf}, journal = {Appl. Stoch. Model. Bus. Ind.}, number = 2, pages = {111--136}, publisher = {John Wiley and Sons Ltd.}, volume = 21, year = 2005, url = {http://vis.lbl.gov/~romano/mlgroup/papers/nusvmtutorial.pdf}, issn = {1524-1904}, doi = {http://dx.doi.org/10.1002/asmb.v21:2}, description = {A tutorial on ν-support vector machines}, abstract = {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].}, biburl = {http://www.bibsonomy.org/bibtex/24849dc190c907bcb507aece582e76353/jil}, keywords = {kernel svm tutorial trick} } @inproceedings{lauser03, title = {Automatic multi-label subject indexing in a multilingual environment}, author = {Boris Lauser and Andreas Hotho}, booktitle = {Proc. of the 7th European Conference in Research and Advanced Technology for Digital Libraries, ECDL 2003}, pages = {140-151}, publisher = {Springer}, series = {LNCS}, volume = 2769, year = 2003, url = {http://citeseer.ist.psu.edu/lauser03automatic.html}, file = {}, biburl = {http://www.bibsonomy.org/bibtex/28b298c325c6ecdb9c01e01057464ae2d/jil}, keywords = {svm classification multilabel hotho} } @inproceedings{conf/trec/Lewis01, title = {Applying Support Vector Machines to the TREC-2001 Batch Filtering and Routing Tasks.}, author = {David D. Lewis}, booktitle = {TREC}, year = 2001, url = {http://citeseer.ist.psu.edu/750769.html}, ee = {http://trec.nist.gov/pubs/trec10/papers/daviddlewis-trec2001-draft4.pdf}, date = {2002-05-06}, biburl = {http://www.bibsonomy.org/bibtex/25206a28e84c94a9226969a8232d37c48/jil}, keywords = {svm j optimierung parameter} } @inproceedings{conf/ista/AgeevD03, title = {Support Vector Machine Parameter Optimization for Text Categorization Problems.}, author = {Mikhail S. Ageev and Boris V. Dobrov}, booktitle = {ISTA}, crossref = {conf/ista/2003}, editor = {Mikhail Godlevsky and Stephen W. Liddle and Heinrich C. Mayr}, pages = {165-176}, publisher = {GI}, series = {LNI}, volume = 30, year = 2003, url = {http://www.cir.ru/docs/ips/publications/2003_ista_svm.pdf}, isbn = {3-88579-359-8}, date = {2003-07-08}, biburl = {http://www.bibsonomy.org/bibtex/21946b7254ef1dccdf34c6140ad133c67/jil}, keywords = {j machine learning optimierung parameter svm text} }