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The SVM class

(PECL svm >= 0.1.0)

Introducere

Sinopsisul clasei

SVM {

/* Constants */

const int C_SVC = 0 ;

const int NU_SVC = 1 ;

const int ONE_CLASS = 2 ;

const int EPSILON_SVR = 3 ;

const int NU_SVR = 4 ;

const int KERNEL_LINEAR = 0 ;

const int KERNEL_POLY = 1 ;

const int KERNEL_RBF = 2 ;

const int KERNEL_SIGMOID = 3 ;

const int KERNEL_PRECOMPUTED = 4 ;

const int OPT_TYPE = 101 ;

const int OPT_KERNEL_TYPE = 102 ;

const int OPT_DEGREE = 103 ;

const int OPT_SHRINKING = 104 ;

const int OPT_PROPABILITY = 105 ;

const int OPT_GAMMA = 201 ;

const int OPT_NU = 202 ;

const int OPT_EPS = 203 ;

const int OPT_P = 204 ;

const int OPT_COEF_ZERO = 205 ;

const int OPT_C = 206 ;

const int OPT_CACHE_SIZE = 207 ;

/* Methods */

public __construct ( )

public svm::crossvalidate ( array $problem , int $number_of_folds ) : float

public getOptions ( ) : array

public setOptions ( array $params ) : bool

public svm::train ( array $problem , array $weights = ? ) : SVMModel

}

Constante predefinite

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

Cuprins

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

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