SVM Function Settings

Support Vector Machines

SVMs are non-linear models which can be used for regression or classification problems. The following settings can be access from the SVM Function Settings panel in the Analysis GUI or from the Options GUI.

SVM Type

Classification (SVMDA)

• Nu-SVM optimizes a model with an adjustable parameter Nu [0 -> 1] which indicates the upper bound on the number of misclassifications allowed.

• C-SVC optimizes a model with an adjustable cost function C [0 -> inf] which indicates how strongly misclassifications should be penalized.

Regression (SVM)

• Epsilon-SVR optimizes a model using the adjustable parameters epsilon (upper tolerance on prediction errors) and C (cost of prediction errors larger than epsilon.)

• Nu-SVR optimizes a model using the adjustable parameter Nu [0 -> 1] which indicates the upper bound on the number of support vectors to use given as a fraction of total calibration samples.