Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes.

The base-classifier-specific options must be passed to the base classifier after a double hyphen ("-").

Example:

java weka.classifiers.meta.CostSensitiveClassifier -W 
weka.classifiers.bayes.BayesNet -t train.arff -T test.arff - -Q 
weka.classifiers.bayes.net.search.global.HillClimber -E 
weka.classifiers.bayes.net.estimate.SimpleEstimator

Here ``-Q ...'' and ``-E ...'' are options to the base classifier.

Source: Weka FAQ