SVM
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The SVM class

はじめに

クラス概要

SVM
class SVM {
/* 定数 */
const int SVM::C_SVC = 0;
const int SVM::NU_SVC = 1;
const int SVM::ONE_CLASS = 2;
const int SVM::EPSILON_SVR = 3;
const int SVM::NU_SVR = 4;
const int SVM::KERNEL_LINEAR = 0;
const int SVM::KERNEL_POLY = 1;
const int SVM::KERNEL_RBF = 2;
const int SVM::KERNEL_SIGMOID = 3;
const int SVM::KERNEL_PRECOMPUTED = 4;
const int SVM::OPT_TYPE = 101;
const int SVM::OPT_KERNEL_TYPE = 102;
const int SVM::OPT_DEGREE = 103;
const int SVM::OPT_SHRINKING = 104;
const int SVM::OPT_PROPABILITY = 105;
const int SVM::OPT_GAMMA = 201;
const int SVM::OPT_NU = 202;
const int SVM::OPT_EPS = 203;
const int SVM::OPT_P = 204;
const int SVM::OPT_COEF_ZERO = 205;
const int SVM::OPT_C = 206;
const int SVM::OPT_CACHE_SIZE = 207;
/* メソッド */
public __construct()
public float svm::crossvalidate(array $problem, int $number_of_folds)
public array getOptions()
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

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