BibliographyType,ISBN,Identifier,Author,Title,Journal,Volume,Number,Month,Pages,Year,Address,Note,URL,Booktitle,Chapter,Edition,Series,Editor,Publisher,ReportType,Howpublished,Institution,Organizations,School,Annote,Custom1,Custom2,Custom3,Custom4,Custom5
6,"1-55860-486-3","joachims1997rocchio","Joachims, Thorsten","A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization.","",,,"","143-151",1997,"","","http://dblp.uni-trier.de/db/conf/icml/icml1997.html#Joachims97","ICML","","","","Fisher, Douglas H.","Morgan Kaufmann","","","","","","","","","bayes estimator laplace probabilistic rocchio tfidf ","",""
10,"","kim2002naive","Kim, S.; Rim, H.; Yook, D. & Lim, H.","Effective methods for improving Naive Bayes text classifiers","",,,"","",2002,"","","http://citeseer.ist.psu.edu/kim02effective.html","","","","","","","","","","","","","","","bayes learning length machine multinomial naive normalization ","",""
6,"","lewis1998naive","Lewis, David D.","Naive (Bayes) at forty: The independence assumption in information retrieval.","",,1398,"","4--15",1998,"Chemnitz, DE","","http://citeseer.ist.psu.edu/lewis98naive.html","Proceedings of {ECML}-98, 10th European Conference on Machine Learning","","","","N{\'{e}}dellec, Claire & Rouveirol, C{\'{e}}line","Springer Verlag, Heidelberg, DE","","","","","","","","","bayes forty ir naive overview representation text ","",""
6,"","mccallum1998naive","McCallum, Andrew & Nigam, Kamal","A Comparison of Event Models for Naive Bayes Text Classification","",,,"","41--48",1998,"","","http://www.kamalnigam.com/papers/multinomial-aaaiws98.pdf","Learning for Text Categorization: Papers from the 1998 {AAAI} Workshop ","","","","","","","","","","","","","","bayes bernoulli classification ereignis event model multinomial naive text vergleich ","",""
10,"","mccallum-multilabel","McCallum, Andrew Kachites","Multi-Label Text Classification with a Mixture Model Trained by EM","",,,"","",1999,"","","http://citeseer.ist.psu.edu/mccallum99multilabel.html","","","","","","","","","","","","","","","bayes classification model multilabel nativ native probabilistic probabilistisch ","",""
10,"","metsis2006naive","Metsis, Vangelis; Androutsopoulos, Ion & Paliouras, Georgios","Spam Filtering with Naive Bayes -- Which Naive Bayes?","",,,"","",2006,"","","http://citeseer.ist.psu.edu/757874.html","","","","","","","","","","","","","","","bayes metsis multinomial multivariate naive spam ","",""
9,"","rennie2001naive","Rennie, Jason D. M.","Improving Multi-class Text Classification with Naive Bayes","",,,"","",2001,"","","http://people.csail.mit.edu/~jrennie/papers/sm-thesis.pdf","","","","","","","","","","","Massachusetts Institute of Technology","","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.","","bayes deduction estimation exhaustive herleitung komplett likelihood map maximum mle multinomial naive prior thesis ","",""