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SVM Learner

Analytics Mining SVM
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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.

Node details

Input ports
  1. Type: Table
    Training Data
    Datatable with training data
Output ports
  1. Type: PMML
    Trained SVM
    Trained Support Vector Machine

Extension

The SVM Learner node is part of this extension:

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