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Navigation: Home \ Development \ Components & Libraries \ Mallet for Windows

Mallet for Windows

2.0.7 by Andrew McCallum

MALLET is a Java-based package for statistical natural language processing, document classification, clustering, topic modeling, information extraction, and other machine learning applications to text.

MALLET includes sophisticated tools for document classification: efficient routines for converting text to "features", a wide variety of algorithms (including Naïve Bayes, Maximum Entropy, and Decision Trees).

type: Freeware
categories: Java package, document classification, extract information, classification, document, tag

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Software Info
Best Vista Download periodically updates pricing and software information of Mallet for Windows full version from the publisher, but some information may be out-of-date. You should confirm all information. Software piracy is theft, using crack, warez passwords, patches, serial numbers, registration codes, key generator, keymaker or keygen for Mallet for Windows license key is illegal and prevent future development of Mallet for Windows. Download links are directly from our mirrors or publisher's website, Mallet for Windows torrent files or shared files from rapidshare, yousendit or megaupload are not allowed!
Released:	March 03, 2012
Filesize:	13.00 MB
Platform:	Windows XP, Windows Vista, Windows 7
Install	Instal And Uninstall

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Mallet for Windows

- Releases History

Software:	Mallet for Windows 2.0.7
Date Released:	Mar 3, 2012
Status:	New Release
Release Notes:	* Fixed a bug in the Generalized Expectation (GE) implementation for MaxEnt models. The old code could give low accuracy when using a small number of constraints. See the note at the top of this page for more information: http://mallet.cs.umass.edu/ge-classification.php * Fixed a bug in SVMLight2Vectors that could result in different Alphabets when importing multiple files at once. * Fixed a bug in SVMLight2Classify that allowed previously unobserved features to be added to the data Alphabet, possibly resulting in mismatching Classifier and InstanceList Alphabets. * Fixed bugs in the search direction computation in ConjugateGradient. * Added support for cross-validation in Vectors2Classify (in addition to random subsamples of the data set). * Added support for importing SVMLight data with Alphabets for which growth is stopped. * Added new options to Optimizers: it is now possible to set the convergence tolerance for GradientAscent, and set the LineOptimizer for LimitedMemoryBFGS