1 / 8

Bayesian Document Classification: Learning and Prediction

This project involves learning to classify documents using a Bayesian method to calculate conditional probability, utilizing a set of training documents and a selected set of features. The background includes the Naïve Bayes Classifier/Bayesian Method, which computes conditional probability for each topic in a given document and assigns it to the topic with the highest probability. Development includes two main steps: learning with training documents and prediction to determine the topic of unknown documents. Expect the program to improve over time as it learns and refines its categorization formulas. Works cited include resources on Bayesian generalized linear models for text categorization.

margaritaw
Download Presentation

Bayesian Document Classification: Learning and Prediction

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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>.

More Related