SVM(3) 1 SVM(3)
The SVM class
INTRODUCTION
CLASS SYNOPSIS
SVM
SVM
Constants
o const integer$SVM::C_SVC0
o const integer$SVM::NU_SVC1
o const integer$SVM::ONE_CLASS2
o const integer$SVM::EPSILON_SVR3
o const integer$SVM::NU_SVR4
o const integer$SVM::KERNEL_LINEAR0
o const integer$SVM::KERNEL_POLY1
o const integer$SVM::KERNEL_RBF2
o const integer$SVM::KERNEL_SIGMOID3
o const integer$SVM::KERNEL_PRECOMPUTED4
o const integer$SVM::OPT_TYPE101
o const integer$SVM::OPT_KERNEL_TYPE102
o const integer$SVM::OPT_DEGREE103
o const integer$SVM::OPT_SHRINKING104
o const integer$SVM::OPT_PROPABILITY105
o const integer$SVM::OPT_GAMMA201
o const integer$SVM::OPT_NU202
o const integer$SVM::OPT_EPS203
o const integer$SVM::OPT_P204
o const integer$SVM::OPT_COEF_ZERO205
o const integer$SVM::OPT_C206
o const integer$SVM::OPT_CACHE_SIZE207
Methods
o public SVM::__construct (void )
o public float svm::crossvalidate (array $problem, int $number_of_folds)
o public array SVM::getOptions (void )
o public bool SVM::setOptions (array $params)
o public SVMModel svm::train (array $problem, [array $weights])
PREDEFINED CONSTANTS
SVM CONSTANTS
o SVM::C_SVC -The basic C_SVC SVM type. The default, and a good starting point
o SVM::NU_SVC -The NU_SVC type uses a different, more flexible, error weighting
o SVM::ONE_CLASS -One class SVM type. Train just on a single class, using outliers as negative examples
o SVM::EPSILON_SVR -A SVM type for regression (predicting a value rather than just a class)
o SVM::NU_SVR -A NU style SVM regression type
o SVM::KERNEL_LINEAR -A very simple kernel, can work well on large document classification problems
o SVM::KERNEL_POLY -A polynomial kernel
o SVM::KERNEL_RBF -The common Gaussian RBD kernel. Handles non-linear problems well and is a good default for classification
o 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
o SVM::KERNEL_PRECOMPUTED -A precomputed kernel - currently unsupported.
o SVM::OPT_TYPE -The options key for the SVM type
o SVM::OPT_KERNEL_TYPE -The options key for the kernel type
o SVM::OPT_DEGREE -
o SVM::OPT_SHRINKING -Training parameter, boolean, for whether to use the shrinking heuristics
o SVM::OPT_PROBABILITY -Training parameter, boolean, for whether to collect and use probability estimates
o SVM::OPT_GAMMA -Algorithm parameter for Poly, RBF and Sigmoid kernel types.
o SVM::OPT_NU -The option key for the nu parameter, only used in the NU_ SVM types
o SVM::OPT_EPS -The option key for the Epsilon parameter, used in epsilon regression
o SVM::OPT_P -Training parameter used by Episilon SVR regression
o SVM::OPT_COEF_ZERO -Algorithm parameter for poly and sigmoid kernels
o SVM::OPT_C -The option for the cost parameter that controls tradeoff between errors and generality - effectively the penalty for
misclassifying training examples.
o SVM::OPT_CACHE_SIZE -Memory cache size, in MB
PHP Documentation Group SVM(3)