Bayesian Network Classifiers in Java / News: Recent posts

Source code transitioned to Subversion

The version control for the jBNC project has been transitioned from CVS to Subversion. Instructions on how to access the source code using Subversion are available at:
http://sourceforge.net/svn/?group_id=49871

Posted by Jarek Sacha 2006-10-22

jBNC v.1.2.1 and jBNC-WEKA v.1.2

jBNC and jBNC-WEKA were updated to support WEKA 3.4.2.

Additionally jBNC-WEKA now supports KnowledgeFlow.

For more details see release notes at the download page (https://sourceforge.net/project/showfiles.php?group_id=49871)

Posted by Jarek Sacha 2004-06-13

JavaBayes v.0346.1

Fabio Cozman's JavaBayes v. 0.346 with partial support for serialization.
For more information on JavaBayes see:
http://www-2.cs.cmu.edu/%7Ejavabayes/index.html/

JavaBayes module was added to jBNC project CVS, source and binary distributions are available through the file release system.

Correction to the original JavaBayes source code were necessary to enable
WEKA 3.4 (http://www.cs.waikato.ac.nz/~ml/) support in jBNC-WEKA.

Posted by Jarek Sacha 2004-06-11

jBNC-WEKA v.1.0 released

jBNC-WEKA provides WEKA bindings for jBNC. WEKA is the Waikato Environment for Knowledge Analysis (http://www.cs.waikato.ac.nz/~ml). jBNC-WEKA allows running jBNC classifiers and utilities from within WEKA, in particular from within its graphical user interface called Explorer.

Posted by Jarek Sacha 2003-07-02

jBNC v.1.1 released

jBNC is a Java toolkit for training, testing, and applying Bayesian Network Classifiers. Implemented classifiers have been shown to perform well in a variety of artificial intelligence, machine learning, and data mining applications.

A major change is added support for Integration with WEKA (http://www.cs.waikato.ac.nz/~ml). Actual binding are provided by a sperate package called jBNC-WEKA.

Posted by Jarek Sacha 2003-07-02

jBNC v.1.0 released.

jBNC is a Java toolkit for training, testing, and applying Bayesian Network Classifiers. Implemented classifiers have been shown to perform well in a variety of artificial intelligence, machine learning, and data mining applications.

This version adds documentation files, Ant build file, and sample datasets.

Posted by Jarek Sacha 2003-01-04

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