@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 = {length machine learning bayes normalization naive multinomial}
}

@mastersthesis{rennie2001naive,
        title = {Improving Multi-class Text Classification with Naive Bayes},
        author = {Jason D. M. Rennie},
        school = {Massachusetts Institute of Technology},
        year = 2001,
        url = {http://people.csail.mit.edu/~jrennie/papers/sm-thesis.pdf},
        abstract = {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.},
	biburl = {http://www.bibsonomy.org/bibtex/22896eb9538a6ee34f8e6c6757bdcf99e/jil},
	keywords = {multinomial bayes estimation map komplett deduction prior naive exhaustive thesis likelihood maximum mle herleitung}
}

@inproceedings{mccallum1998naive,
        title = {A Comparison of Event Models for Naive {B}ayes Text Classification},
        author = {Andrew McCallum and Kamal Nigam},
        booktitle = {Learning for Text Categorization: Papers from the 1998 {AAAI} Workshop },
        pages = {41--48},
        year = 1998,
        url = {http://www.kamalnigam.com/papers/multinomial-aaaiws98.pdf},
        biburl = {http://www.bibsonomy.org/bibtex/2fa46d1cc0dd56ab40a7f722e569a1fd3/jil},
	keywords = {classification text vergleich model naive event ereignis multinomial bernoulli bayes}
}

@inproceedings{lewis1998naive,
        title = {Naive ({B}ayes) at forty: The independence assumption in information retrieval.},
        address = {Chemnitz, DE},
        author = {David D. Lewis},
        booktitle = {Proceedings of {ECML}-98, 10th European Conference on Machine Learning},
        editor = {Claire N{\'{e}}dellec and C{\'{e}}line Rouveirol},
        number = 1398,
        pages = {4--15},
        publisher = {Springer Verlag, Heidelberg, DE},
        year = 1998,
        url = {http://citeseer.ist.psu.edu/lewis98naive.html},
        biburl = {http://www.bibsonomy.org/bibtex/2e290abb350b7aa09a412c1dddac55cd6/jil},
	keywords = {ir forty overview text representation naive bayes}
}

@misc{metsis2006naive,
        title = {Spam Filtering with Naive Bayes -- Which Naive Bayes?},
        author = {Vangelis Metsis and Ion Androutsopoulos and Georgios Paliouras},
        year = 2006,
        url = {http://citeseer.ist.psu.edu/757874.html},
        biburl = {http://www.bibsonomy.org/bibtex/2b4e1a9d4635a9fb1f11a947f1ab3618a/jil},
	keywords = {spam naive bayes multivariate multinomial metsis}
}