http://www.bibsonomy.org/burst/user/jil/multinomialBibSonomy BuRST Feed for /user/jil/multinomial2008-10-16T09:26:01+02:00http://www.bibsonomy.org/bibtex/2b8f819dc681e76ee9723c72a859dff3c/jiljil2008-05-06T02:13:03+02:00naive multinomial normalization length bayes learning machine <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>)Tue May 06 02:13:03 CEST 2008Effective methods for improving Naive Bayes text classifiers2002naive multinomial normalization length bayes learning machine http://www.bibsonomy.org/bibtex/22896eb9538a6ee34f8e6c6757bdcf99e/jiljil2008-05-05T19:34:57+02:00herleitung multinomial estimation bayes exhaustive likelihood thesis prior naive komplett maximum map deduction mle <span style="color:#555555;">Jason D. M. <a href="http://www.bibsonomy.org/author/Rennie">Rennie</a> </span>(<em>2001</em>)Mon May 05 19:34:57 CEST 2008Improving Multi-class Text Classification with Naive Bayes2001herleitung multinomial estimation bayes exhaustive likelihood thesis prior naive komplett maximum map deduction mle There 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+02:00naive text model multinomial event ereignis classification vergleich bernoulli bayes <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>)Mon May 05 19:02:36 CEST 2008Learning for Text Categorization: Papers from the 1998 {AAAI} Workshop 41--48A Comparison of Event Models for Naive {B}ayes Text Classification1998naive text model multinomial event ereignis classification vergleich bernoulli bayes http://www.bibsonomy.org/bibtex/2b4e1a9d4635a9fb1f11a947f1ab3618a/jiljil2008-05-05T18:50:15+02:00naive multinomial multivariate bayes spam metsis <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>)Mon May 05 18:50:15 CEST 2008Spam Filtering with Naive Bayes -- Which Naive Bayes?2006naive multinomial multivariate bayes spam metsis