The SVM class

(PECL svm >= 0.1.0)

Introduction

Class synopsis

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 */
public __construct ( void )
public svm::crossvalidate ( array $problem , int $number_of_folds ) : float
public getOptions ( void ) : array
public setOptions ( array $params ) : bool
public svm::train ( array $problem [, array $weights ] ) : SVMModel
}

Predefined Constants

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

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