The following node is available in the Open Source KNIME predictive analytics and data mining platform version 2.7.1. Discover over 1000 other nodes, as well as enterprise functionality at http://knime.com.
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.
0 | Datatable with training data |
1 | Optional PMML port object containing preprocessing operations. |
0 | Trained Support Vector Machine |