例
SVM::__construct
SVM
PHP Manual

# The SVM class

(PECL svm >= 0.1.0)

## 導入

## クラス概要

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 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` ] )
}

## 定義済み定数

## 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

## 目次

- SVM::__construct — SVM オブジェクトを新規構築
- 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

例
SVM::__construct
SVM
PHP Manual