The SVM class

&reftitle.classsynopsis; SVM SVM Constants const int SVM::C_SVC 0 const int SVM::NU_SVC 1 const int SVM::ONE_CLASS 2 const int SVM::EPSILON_SVR 3 const int SVM::NU_SVR 4 const int SVM::KERNEL_LINEAR 0 const int SVM::KERNEL_POLY 1 const int SVM::KERNEL_RBF 2 const int SVM::KERNEL_SIGMOID 3 const int SVM::KERNEL_PRECOMPUTED 4 const int SVM::OPT_TYPE 101 const int SVM::OPT_KERNEL_TYPE 102 const int SVM::OPT_DEGREE 103 const int SVM::OPT_SHRINKING 104 const int SVM::OPT_PROPABILITY 105 const int SVM::OPT_GAMMA 201 const int SVM::OPT_NU 202 const int SVM::OPT_EPS 203 const int SVM::OPT_P 204 const int SVM::OPT_COEF_ZERO 205 const int SVM::OPT_C 206 const int SVM::OPT_CACHE_SIZE 207 Methods

&reftitle.constants;

SVM Constants 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