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Veritas VxVM & Vxfs to SVM and UFS
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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)
