Type of a SVM formulation.

Default value is C_SVC. Possible values:

• C_SVC C-Support Vector Classification. n-class classification (n>=2), allows imperfect separation of classes with penalty multiplier C for outliers.
• NU_SVC Nu-Support Vector Classification. n-class classification with possible imperfect separation. Parameter Nu (in the range 0..1, the larger the value, the smoother the decision boundary) is used instead of C.
• ONE_CLASS Distribution Estimation (One-class SVM). All the training data are from the same class, SVM builds a boundary that separates the class from the rest of the feature space.
• EPS_SVR P-Support Vector Regression. The distance between feature vectors from the training set and the fitting hyper-plane must be less than P. For outliers the penalty multiplier C is used.
• NU_SVR Nu-Support Vector Regression. Nu is used instead of P. See [LibSVM] for details.