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(PECL svm >= 0.1.0)
$problem
, int $number_of_folds
)$params
)$problem
[, array $weights
] )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