author: shucheng cui
Description: This repository incorporates some basic java examples how to
use the weka java library to implement machine learning algorithms. Please
refer to the weka user documents to explore more detail information.
1. aggregation.java: example of using AdaBoost, bagging, stacking and voting. 
2. attributes.java: example of data atrributes
3. classificationprediction.java : example of using classifier to predict the class of one instance.
4. classifier.java: example of using svm to make prediction
5. cluster.java: example of using cluster to make prediction
6. copyofclassificationprediction.java: example of how to write the prediction result back to file.
7. crossvalidation.java: example of using cross validation to make model choice.
8. evaluate.java: example of using training data to train model and apply in test data.
9. normalization.java: example of using filter to scale the training data
10. outputpredicttotestfile.java: example of how to write the prediction result back to file.
15. regression.java: example of using regression classifier to make prediction.
16. saveloadmodel.java: example of saving training model to file and load the model to apply in test data.
17. sparsedata.java: example of how to using the sparse format in training data to save storage space.
18. CSV2Arff.java: example of converting csv file to arff file.