Hi everyone!
New versions of Weka are available for download from the Weka homepage:
* Weka 3.6.10 - stable book 3rd edition version. It is available as ZIP, with Win32 installer, Win32 installer incl. JRE 1.7.0_25, Win64 installer, Win64 installer incl. 64 bit JRE 1.7.0_25 and Mac OS X application (both Oracle and Apple JVM versions).
* Weka 3.7.10 - development version. It is available as ZIP, with Win32 installer, Win32 installer incl. JRE 1.7.0_25, Win64 installer, Win64 installer incl. 64 bit JRE 1.7.0_25 and Mac OS X application (both Oracle and Apple JVM versions).
Both versions contain a significant number of bug fixes, it is recommended to upgrade to the new versions. Stable Weka 3.6 receives bug fixes only. The development version receives bug fixes and new features.
Weka homepage:
http://www.cs.waikato.ac.nz/~ml/weka/
Pentaho data mining community documentation:
http://wiki.pentaho.com/display/Pent...+Documentation
Packages for Weka>=3.7.2 can be browsed online at:
http://weka.sourceforge.net/packageMetaData/
The Pentaho Weka micro site at http://weka.pentaho.com/ will be updated to reflect the new releases soon.
Note: It might take a while before Sourceforge.net has propagated all the files to its mirrors.
What's new in 3.7.10?
Some highlights
---------------
In core weka:
* HoeffdingTree. Ported from the MOA implementation to a Weka classifier
* MergeInfrequentNominalValues filter
* MergeNominalValues filter. Uses an CHAID-style merging routine
* Zoom facility in the Knowledge Flow
* Epsilon-insensitive and Huber loss functions in SGD
* More CSVLoader improvements
* Class specific IR metric based evaluation in WrapperSubsetEval
* GainRatioAttributeEval now supports instance weights
* New command line option to force batch training mode when the classifier is an incremental one
* LinearRegression is now faster and more memory efficient thanks to a contribution from Sean Daugherty
* CfsSubsetEval can now use multiple CPUs/cores to pre-compute the correlation matrix (speeds up backward searches)
* GreedyStepwise can now evaluate mutliple subsets in parallel
In packages:
* New kernelLogisticRegression package
* New supervisedAttributeScaling package
* New clojureClassifier package
* localOutlierFactor now includes a wrapper classifier that uses the LOF filter
* scatterPlot3D now includes new Java3D libraries for all major platforms
* New IWSS (Incremental Wrapper Subset Selection) package contributed by Pablo Bermejo
* New MODLEM package (rough set theory based rule induction) contributed by Szymon Wojciechowski
As usual, for a complete list of changes refer to the changelogs.
Cheers,
The Weka Team
New versions of Weka are available for download from the Weka homepage:
* Weka 3.6.10 - stable book 3rd edition version. It is available as ZIP, with Win32 installer, Win32 installer incl. JRE 1.7.0_25, Win64 installer, Win64 installer incl. 64 bit JRE 1.7.0_25 and Mac OS X application (both Oracle and Apple JVM versions).
* Weka 3.7.10 - development version. It is available as ZIP, with Win32 installer, Win32 installer incl. JRE 1.7.0_25, Win64 installer, Win64 installer incl. 64 bit JRE 1.7.0_25 and Mac OS X application (both Oracle and Apple JVM versions).
Both versions contain a significant number of bug fixes, it is recommended to upgrade to the new versions. Stable Weka 3.6 receives bug fixes only. The development version receives bug fixes and new features.
Weka homepage:
http://www.cs.waikato.ac.nz/~ml/weka/
Pentaho data mining community documentation:
http://wiki.pentaho.com/display/Pent...+Documentation
Packages for Weka>=3.7.2 can be browsed online at:
http://weka.sourceforge.net/packageMetaData/
The Pentaho Weka micro site at http://weka.pentaho.com/ will be updated to reflect the new releases soon.
Note: It might take a while before Sourceforge.net has propagated all the files to its mirrors.
What's new in 3.7.10?
Some highlights
---------------
In core weka:
* HoeffdingTree. Ported from the MOA implementation to a Weka classifier
* MergeInfrequentNominalValues filter
* MergeNominalValues filter. Uses an CHAID-style merging routine
* Zoom facility in the Knowledge Flow
* Epsilon-insensitive and Huber loss functions in SGD
* More CSVLoader improvements
* Class specific IR metric based evaluation in WrapperSubsetEval
* GainRatioAttributeEval now supports instance weights
* New command line option to force batch training mode when the classifier is an incremental one
* LinearRegression is now faster and more memory efficient thanks to a contribution from Sean Daugherty
* CfsSubsetEval can now use multiple CPUs/cores to pre-compute the correlation matrix (speeds up backward searches)
* GreedyStepwise can now evaluate mutliple subsets in parallel
In packages:
* New kernelLogisticRegression package
* New supervisedAttributeScaling package
* New clojureClassifier package
* localOutlierFactor now includes a wrapper classifier that uses the LOF filter
* scatterPlot3D now includes new Java3D libraries for all major platforms
* New IWSS (Incremental Wrapper Subset Selection) package contributed by Pablo Bermejo
* New MODLEM package (rough set theory based rule induction) contributed by Szymon Wojciechowski
As usual, for a complete list of changes refer to the changelogs.
Cheers,
The Weka Team