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The SVM class
(Информация о версии неизвестна, возможно, только в SVN)
Введение
Обзор классов
SVM {
/* Constants */
const integer C_SVC = 0 ;
const integer NU_SVC = 1 ;
const integer ONE_CLASS = 2 ;
const integer EPSILON_SVR = 3 ;
const integer NU_SVR = 4 ;
const integer KERNEL_LINEAR = 0 ;
const integer KERNEL_POLY = 1 ;
const integer KERNEL_RBF = 2 ;
const integer KERNEL_SIGMOID = 3 ;
const integer KERNEL_PRECOMPUTED = 4 ;
const integer OPT_TYPE = 101 ;
const integer OPT_KERNEL_TYPE = 102 ;
const integer OPT_DEGREE = 103 ;
const integer OPT_SHRINKING = 104 ;
const integer OPT_PROPABILITY = 105 ;
const integer OPT_GAMMA = 201 ;
const integer OPT_NU = 202 ;
const integer OPT_EPS = 203 ;
const integer OPT_P = 204 ;
const integer OPT_COEF_ZERO = 205 ;
const integer OPT_C = 206 ;
const integer OPT_CACHE_SIZE = 207 ;
/* Methods */
__construct ( void )
public float svm::crossvalidate ( array $problem , int $number_of_folds )
public array getOptions ( void )
public bool setOptions ( array $params )
public SVMModel svm::train ( array $problem [, array $weights ] )
}
Предопределенные константы
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
Содержание
- SVM::__construct — Construct a new SVM object
- SVM::crossvalidate — Test training params on subsets of the training data.
- SVM::getOptions — Return the current training parameters
- SVM::setOptions — Set training parameters
- SVM::train — Create a SVMModel based on training data
Описание класса svm, примеры использования класса svm.
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