SMOreg (3.6) (legacy)

SMOreg implements the support vector machine for regression. The parameters can be learned using various algorithms. The algorithm is selected by setting the RegOptimizer. The most popular algorithm (RegSMOImproved) is due to Shevade, Keerthi et al and this is the default RegOptimizer. For more information see: S.K. Shevade, S.S. Keerthi, C. Bhattacharyya, K.R.K. Murthy: Improvements to the SMO Algorithm for SVM Regression. In: IEEE Transactions on Neural Networks, 1999. A.J. Smola, B. Schoelkopf (1998). A tutorial on support vector regression.

(based on WEKA 3.6)

For further options, click the 'More' - button in the dialog.

All weka dialogs have a panel where you can specify classifier-specific parameters.

Input ports

1. Type: Table
   Training data
   Training data

Output ports

1. Type: Weka 3.6 Classifier
   Trained classifier
   Trained classifier