http://www.bibsonomy.org/burst/user/jilBibSonomy BuRST Feed for /user/jil2008-05-14T13:12:07+02:00http://www.bibsonomy.org/bibtex/2dfa99d567392038673882c932153053c/jiljil2008-05-08T19:46:13+02:00bayes estimator laplace probabilistic rocchio tfidf <span style="color:#555555;">Thorsten <a href="http://www.bibsonomy.org/author/Joachims">Joachims</a> </span><em>ICML, </em><em>Seite143-151. </em><em>Morgan Kaufmann, </em>(<em>1997</em>)ICMLconf/icml/1997143-151A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization.1997bayesestimatorlaplaceprobabilisticrocchiotfidfhttp://www.bibsonomy.org/bibtex/2e46f97a70e986c33b1822d6a247dd1a5/jiljil2008-05-08T18:07:32+02:00average classification classifier cos cosinus interpretation klassifikation learning loose machine rocchio similarity simple tight <span style="color:#555555;">Eui-Hong <a href="http://www.bibsonomy.org/author/Han">Han</a> und George <a href="http://www.bibsonomy.org/author/Karypis">Karypis</a> </span><em>PKDD, </em><em>Volume1910vonLecture Notes in Computer Science, </em><em>Seite424-431. </em><em>Springer, </em>(<em>2000</em>)PKDDconf/pkdd/2000424-431Lecture Notes in Computer ScienceCentroid-Based Document Classification: Analysis and Experimental Results.19102000averageclassificationclassifiercoscosinusinterpretationklassifikationlearningloosemachinerocchiosimilaritysimpletighthttp://www.bibsonomy.org/bibtex/2b8f819dc681e76ee9723c72a859dff3c/jiljil2008-05-06T02:13:03+02:00bayes learning length machine multinomial naive normalization <span style="color:#555555;">S. <a href="http://www.bibsonomy.org/author/Kim">Kim</a> und H. <a href="http://www.bibsonomy.org/author/Rim">Rim</a> und D. <a href="http://www.bibsonomy.org/author/Yook">Yook</a> und H. <a href="http://www.bibsonomy.org/author/Lim">Lim</a> </span>(<em>2002</em>)Effective methods for improving Naive Bayes text classifiers2002bayeslearninglengthmachinemultinomialnaivenormalizationhttp://www.bibsonomy.org/bibtex/22896eb9538a6ee34f8e6c6757bdcf99e/jiljil2008-05-05T19:34:57+02:00bayes deduction estimation exhaustive herleitung komplett likelihood map maximum mle multinomial naive prior thesis <span style="color:#555555;">Jason D. M. <a href="http://www.bibsonomy.org/author/Rennie">Rennie</a> </span>(<em>2001</em>)Improving Multi-class Text Classification with Naive Bayes2001bayesdeductionestimationexhaustiveherleitungkomplettlikelihoodmapmaximummlemultinomialnaivepriorthesisThere are numerous text documents available in electronic form. More and more are becoming available every day. Such documents represent a massive amount of information that is easily accessible. Seeking value in this huge collection requires organization; much of the work of organizing documents can be automated through text classification. The accuracy and our understanding of such systems greatly influences their usefulness. In this paper, we seek 1) to advance the understanding of commonly used text classification techniques, and 2) through that understanding, improve the tools that are available for text classification. We begin by clarifying the assumptions made in the derivation of Naive Bayes, noting basic properties and proposing ways for its extension and improvement. Next, we investigate the quality of Naive Bayes parameter estimates and their impact on classification. Our analysis leads to a theorem which gives an explanation for the improvements that can be found in multiclass classification with Naive Bayes using Error-Correcting Output Codes. We use experimental evidence on two commonly-used data sets to exhibit an application of the theorem. Finally, we show fundamental flaws in a commonly-used feature selection algorithm and develop a statistics-based framework for text feature selection. Greater understanding of Naive Bayes and the properties of text allows us to make better use of it in text classification.http://www.bibsonomy.org/bibtex/2fa46d1cc0dd56ab40a7f722e569a1fd3/jiljil2008-05-05T19:02:36+02:00bayes bernoulli classification ereignis event model multinomial naive text vergleich <span style="color:#555555;">Andrew <a href="http://www.bibsonomy.org/author/McCallum">McCallum</a> und Kamal <a href="http://www.bibsonomy.org/author/Nigam">Nigam</a> </span><em>Learning for Text Categorization: Papers from the 1998 AAAI Workshop, </em><em>Seite41--48. </em>(<em>1998</em>)Learning for Text Categorization: Papers from the 1998 {AAAI} Workshop 41--48A Comparison of Event Models for Naive {B}ayes Text Classification1998bayesbernoulliclassificationereigniseventmodelmultinomialnaivetextvergleichhttp://www.bibsonomy.org/bibtex/2e290abb350b7aa09a412c1dddac55cd6/jiljil2008-05-05T18:53:49+02:00bayes forty ir naive overview representation text <span style="color:#555555;">David D. <a href="http://www.bibsonomy.org/author/Lewis">Lewis</a> </span><em>Proceedings of ECML-98, 10th European Conference on Machine Learning, </em><em>1398, </em><em>Seite4--15. </em><em>Chemnitz, DE, </em><em>Springer Verlag, Heidelberg, DE, </em>(<em>1998</em>)Chemnitz, DEProceedings of {ECML}-98, 10th European Conference on Machine Learning13984--15Naive ({B}ayes) at forty: The independence assumption in information retrieval.1998bayesfortyirnaiveoverviewrepresentationtexthttp://www.bibsonomy.org/bibtex/2b4e1a9d4635a9fb1f11a947f1ab3618a/jiljil2008-05-05T18:50:15+02:00bayes metsis multinomial multivariate naive spam <span style="color:#555555;">Vangelis <a href="http://www.bibsonomy.org/author/Metsis">Metsis</a> und Ion <a href="http://www.bibsonomy.org/author/Androutsopoulos">Androutsopoulos</a> und Georgios <a href="http://www.bibsonomy.org/author/Paliouras">Paliouras</a> </span>(<em>2006</em>)Spam Filtering with Naive Bayes -- Which Naive Bayes?2006bayesmetsismultinomialmultivariatenaivespamhttp://www.bibsonomy.org/bibtex/2b7cf853e8635bd2887e8dea3d9e10ccb/jiljil2008-05-04T17:00:45+02:00background kkt lagrange math mathe mathematik svm tutorial <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>)A Short SVM (Support Vector Machine) Tutorial2004backgroundkktlagrangemathmathemathematiksvmtutorialhttp://www.bibsonomy.org/bibtex/2ad2a33b52e690eaf15da04fff7f12755/jiljil2008-05-04T16:52:21+02:00burges deduction herleitung kkt lagrange svm tutorial <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>)Data Mining and Knowledge Discovery2121-167A Tutorial on Support Vector Machines for Pattern Recognition21998burgesdeductionherleitungkktlagrangesvmtutorialhttp://www.bibsonomy.org/bibtex/27cf3e7981cac898c1745418db83e0fd6/jiljil2008-05-02T15:34:36+02:00svm svmlight 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>)Bled, SLProceedings of {ICML}-99, 16th International Conference on Machine Learning200--209Transductive Inference for Text Classification using Support Vector Machines1999svmsvmlighttransductiveThis 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.