The SVM class

(PECL svm >= 0.1.0)

Introduction

Class synopsis

class 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
}

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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