Machine Learning Tools

1. MLC++ - Written in C++. A paper introducing this tool is here. If I'm going to study this toolset, I will start from MLC++ utilities.
   
2. WEKA - Written in Java. An introduction of the command line interface to the tool is here. Basically, the model in WEKA can be summarized as follows.
   


• Filters - Filters are responsible for transforming datasets. Two kinds of filters are available. Supervised filtering would take class information into consideration, while unsupervised filtering is the non-stratified version of resampling the dataset.
  
• Instances - Dataset is implemented by Instances class.
  
• Classifier - All the learning algorithms are derived from the abstract Classifier.

WEKA expresses data as a collection of instances. A user applies filters to transform dataset and then feed the output to classifiers for training and testing. To learn how to call WEKA from Java, check out the sample.