SVM Learner

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.

Options

Class column: Choose the column that contains the nominal target variable.
Overlapping penalty: The overlapping penalty is useful in the case that the input data is not separable. It determines how much penalty is assigned to each point that is misclassified. A good value for it is 1.
Kernel type: There are a number of kernels to choose from. Each kernel has its own parameters, which appear in the configuration dialog just under the kernel.

Input Ports

: Datatable with training data

Output Ports

: Trained Support Vector Machine

Popular Predecessors

Popular Successors

Views

SVM View: Shows the trained Support Vector Machines for each class with their corresponding support vectors.

Workflows

Developers

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Installation

To use this node in KNIME, install the extension KNIME Base nodes from the below update site following our NodePit Product and Node Installation Guide:

v5.3

A zipped version of the software site can be downloaded here.

Plugin provider: KNIME AG, Zurich, Switzerland

Plugin version: 5.3.3.v202410291553

On NodePit since: 2024-07-09

Last update: 2024-11-20

KNIME versions: Since v3.6

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