What's new in Weka 3.8.0 and 3.9.0

In core weka

• JAMA-based linear algebra routines replaced with MTJ. Faster operation with the option to use native libraries for even more speed
• General efficiency improvements in core, filters and some classifiers
• GaussianProcesses now handles instance weights
• New Knowledge Flow implementation. Engine completely rewritten from scratch with a simplified API
• New Workbench GUI
• GUI package manager now has a search facility
• FixedDictionaryStringToWordVector filter allows the use of an external dictionary for vectorization. DictionarySaver converter can be used to create a dictionary file

In packages

• Packages that were using JAMA are now using MTJ
• New netlibNativeOSX, netlibNativeWindows and netlibNativeLinux packages providing native reference implementations (and system-optimized implementation in the case of OSX) of BLAS, LAPACK and ARPACK linear algebra
• New elasticNet package, courtesy of Nikhil Kinshore
• New niftiLoader package for loading a directory with MIR data in NIfTI format into Weka
• New percentageErrorMetrics package - provides plugin evaluation metrics for root mean square percentage error and mean absolute percentage error
• New iterativeAbsoluteErrorRegression package - provides a meta learner that fits a regression model to minimize absolute error
• New largeScaleKernelLearning package - contains filters for large-scale kernel-based learning
• discriminantAnalysis package now contains an implementation for LDA and QDA
• New Knowledge Flow component implementations in various packages
• newKnowledgeFlowStepExamples package - contains code examples for new Knowledge Flow API discussion in the Weka Manual
• RPlugin updated to latest version of MLR
• scatterPlot3D and associationRulesVisualizer packages updated with latest Java 3D libraries
• Support for pluggable activation functions in the multiLayerPerceptrons package