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Learning to Classify Documents Edwin Zhang Computer Systems Lab 2009-2010

Learning to Classify Documents Edwin Zhang Computer Systems Lab 2009-2010. Abstract. Classifying documents Will use a Bayesian method and calculate conditional probability Use a set of Training Documents Choose a set of features. Introduction. Learning to Classify Documents

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Learning to Classify Documents Edwin Zhang Computer Systems Lab 2009-2010

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  1. Learning to Classify DocumentsEdwin ZhangComputer Systems Lab 2009-2010

  2. Abstract • Classifying documents • Will use a Bayesian method and calculate conditional probability • Use a set of Training Documents • Choose a set of features

  3. Introduction • Learning to Classify Documents • Use a Bayesian Method • Code in Python/Java

  4. Background • Naïve Bayes Classifier/Bayesian Method • computes the conditional probability p(T|D) for a given document D for every topic • Assigns the document D to the topic with the largest conditional probability http://nltk.googlecode.com/svn/trunk/doc/book/ch06.html

  5. Development • Program has two steps: • Learning • Prediction • Learning • training documents • conditional probability • features selection http://www.dot.state.mn.us/consult/images/j0341469.jpg

  6. Development • Prediction • Predicting what a unknown document is talking about based on prediction section http://www.deafsports.co.nz/WebImages/documents.jpg

  7. Expected Results • Initially, the program may have trouble classifying documents into the correct category • As the program learns more and improves its formulas, it will get better at classifying documents into the correct categories.

  8. Works Cited • http://www.nltk.org/book • My dad • Eyheramendy, Susana, and David Madigan. "A Flexible Bayesian Generalized Linear Model for Dichotomous Response Data with an Application to Text Categorization." Lecture Notes-Monograph Series 54 (2007): 76-91. JSTOR. Web. 25 Oct. 2009. <http://www.jstor.org/stable/20461460>.

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