SVM::C_SVC-
The basic C_SVC SVM type. The default, and a good starting point
SVM::NU_SVC-
The NU_SVC type uses a different, more flexible, error weighting
SVM::ONE_CLASS-
One class SVM type. Train just on a single class, using outliers as negative examples
SVM::EPSILON_SVR-
A SVM type for regression (predicting a value rather than just a class)
SVM::NU_SVR-
A NU style SVM regression type
SVM::KERNEL_LINEAR-
A very simple kernel, can work well on large document classification problems
SVM::KERNEL_POLY-
A polynomial kernel
SVM::KERNEL_RBF-
The common Gaussian RBD kernel. Handles non-linear problems well and is a good default for classification
SVM::KERNEL_SIGMOID-
A kernel based on the sigmoid function. Using this makes the SVM very similar to a two layer sigmoid based neural network
SVM::KERNEL_PRECOMPUTED-
A precomputed kernel - currently unsupported.
SVM::OPT_TYPE-
The options key for the SVM type
SVM::OPT_KERNEL_TYPE-
The options key for the kernel type
SVM::OPT_DEGREE-
SVM::OPT_SHRINKING-
Training parameter, boolean, for whether to use the shrinking heuristics
SVM::OPT_PROBABILITY-
Training parameter, boolean, for whether to collect and use probability estimates
SVM::OPT_GAMMA-
Algorithm parameter for Poly, RBF and Sigmoid kernel types.
SVM::OPT_NU-
The option key for the nu parameter, only used in the NU_ SVM types
SVM::OPT_EPS-
The option key for the Epsilon parameter, used in epsilon regression
SVM::OPT_P-
Training parameter used by Episilon SVR regression
SVM::OPT_COEF_ZERO-
Algorithm parameter for poly and sigmoid kernels
SVM::OPT_C-
The option for the cost parameter that controls tradeoff between errors and generality - effectively the penalty for misclassifying training examples.
SVM::OPT_CACHE_SIZE-
Memory cache size, in MB