@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 = {guide tutorial svm libsvm} } @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 = {joachims svmlight svm light svm_light} } @inproceedings{han2000rocchio, title = {Centroid-Based Document Classification: Analysis and Experimental Results.}, author = {Eui-Hong Han and George Karypis}, booktitle = {PKDD}, crossref = {conf/pkdd/2000}, editor = {Djamel A. Zighed and Henryk Jan Komorowski and Jan M. Zytkow}, pages = {424-431}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, volume = 1910, year = 2000, url = {http://glaros.dtc.umn.edu/gkhome/fetch/papers/centroidPKDD00.pdf}, ee = {http://link.springer.de/link/service/series/0558/bibs/1910/19100424.htm}, isbn = {3-540-41066-X}, date = {2002-07-22}, biburl = {http://www.bibsonomy.org/bibtex/2e46f97a70e986c33b1822d6a247dd1a5/jil}, keywords = {cosinus klassifikation cos simple rocchio learning loose machine classification similarity classifier tight interpretation average} } @misc{kim2002naive, title = {Effective methods for improving Naive Bayes text classifiers}, author = {S. Kim and H. Rim and D. Yook and H. Lim}, year = 2002, url = {http://citeseer.ist.psu.edu/kim02effective.html}, biburl = {http://www.bibsonomy.org/bibtex/2b8f819dc681e76ee9723c72a859dff3c/jil}, keywords = {naive multinomial normalization length bayes learning machine} } @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 = {mathematik math lagrange kkt background tutorial svm mathe} } @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 burges kkt tutorial deduction svm} } @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 svm transductive} } @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 = {linear optimierung reformulation optimization svm} } @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 = {tutorial svm trick kernel} } @article{sebastiani2002, title = {Machine learning in automated text categorization}, author = {F. Sebastiani}, editor = { http://arxiv.org/pdf/cs.IR/0110053.pdf}, journal = {ACM Computing Surveys}, number = 1, pages = {1--47}, volume = 34, year = 2002, url = {http://nmis.isti.cnr.it/sebastiani/Publications/ACMCS02.pdf}, address = {--Dordrecht--London}, biburl = {http://www.bibsonomy.org/bibtex/2966f2760e840f6b325ad4b3167810dda/jil}, keywords = {klassifikation text sebastiani algorithmen classification vergleich learning machine kdd categorization} } @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 = {hotho classification svm multilabel} } @inproceedings{schapire98rocchio, title = {Boosting and Rocchio applied to text filtering.}, address = {Melbourne, Australia}, author = {Robert E. Schapire and Yoram Singer and Amit Singhal}, booktitle = {Proceedings of {SIGIR}-98, 21st {ACM} International Conference on Research and Development in Information Retrieval}, pages = {215--223}, publisher = {ACM Press, New York, US}, year = 1998, url = {http://singhal.info/rocboost.pdf}, biburl = {http://www.bibsonomy.org/bibtex/2711d31d265daf4bb9c3453f8a87727c0/jil}, keywords = {klassifikator classification rocchio classifier learning shapire machine} } @inproceedings{lewis1995, title = {Text Categorization of Low Quality Images}, author = {David J. Ittner and David D. Lewis and David D. Ahn}, pages = {301-315}, year = 1995, url = {http://staff.science.uva.nl/~ahn/pub/sdair.pdf}, biburl = {http://www.bibsonomy.org/bibtex/214c1324528d8b10a4434c05a7446a7c7/jil}, keywords = {klassifikator classification rocchio classifier learning machine} } @inproceedings{larkey99patent, title = {A patent search and classification system}, address = {Berkeley, US}, author = {Leah S. Larkey}, booktitle = {Proceedings of {DL}-99, 4th {ACM} Conference on Digital Libraries}, editor = {Edward A. Fox and Neil Rowe}, pages = {179--187}, publisher = {ACM Press, New York, US}, year = 1999, url = {http://citeseer.ist.psu.edu/larkey99patent.html}, biburl = {http://www.bibsonomy.org/bibtex/25971173b581b89d64729fff06a02b6bc/jil}, keywords = {category patent knn pivoted larkey} } @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 = {parameter optimierung svm j} } @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 = {text parameter optimierung svm learning machine j} }