Stanford Classifer 2.1.4

A classifier is a machine learning tool that will take data items and place them into one of k classes. A probabilistic classifier, like this one, can also give a probability distribution over the class assignment for a data item. This software is a Java implementation of a maximum entropy classifier. Maximum entropy models are otherwise known as conditional loglinear models, and are essentially equivalent to multiclass logistic regression models (though parameterized slightly differently, in a way that is advantageous with sparse explanatory feature vectors). The classification method is described in:<br /><br /> Christopher Manning and Dan Klein. 2003. Optimization, Maxent Models, and Conditional Estimation without Magic. Tutorial at HLT-NAACL 2003 and ACL 2003. [pdf slides] [pdf handouts] <br /><br />Version 2 of the classifier was developed by Anna Rafferty, Alex Kleeman, Jenny Finkel, and Christopher Manning.<br /><br />The software requires requires Java (JDK1.5+). As well as API access, the program includes an easy-to-use command-line interface, ColumnDataClassifier, for building maximum entropy models. Its features are especially suited to building models over text data, but it also supports numeric variables.

Requirements:	No special requirements
Platforms:	Mac, Mac OSX
Keyword:	Developed,  Estimation,  Handouts,  Hltnaacl,  Magic,  Rafferty,  Slides,  Tutorial,  Version
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