SVM Learner (deprecated)

This node trains a support vector machine on the input data. It supports a number of different kernels (HyperTangent, Polynomial and RBF). The SVM learner supports multiple class problems as well (by computing the hyperplane between each class and the rest), but note that this will increase the runtime.

The SVM learning algorithm used is described in the following papers: Fast Training of Support Vector Machines using Sequential Minimal Optimization , by John C. Platt and Improvements to Platt's SMO Algorithm for SVM Classifier Design , by S. S. Keerthi et. al.

If the optional PMML inport is connected and contains preprocessing operations in the TransformationDictionary those are added to the learned model.

Input ports

1. Type: Table
   Training Data
   Datatable with training data
2. Type: PMML
   PMML Preprocessing
   Optional PMML port object containing preprocessing operations.

Output ports

1. Type: PMML
   Trained SVM
   Trained Support Vector Machine