Support Vector Machine

Table of Contents

Introduction
Installing/Configuring
Examples
SVM — The SVM class
- 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
SVMModel — The SVMModel class
- SVMModel::checkProbabilityModel — Returns true if the model has probability information
- SVMModel::__construct — Construct a new SVMModel
- SVMModel::getLabels — Get the labels the model was trained on
- SVMModel::getNrClass — Returns the number of classes the model was trained with
- SVMModel::getSvmType — Get the SVM type the model was trained with
- SVMModel::getSvrProbability — Get the sigma value for regression types
- SVMModel::load — Load a saved SVM Model
- SVMModel::predict_probability — Return class probabilities for previous unseen data
- SVMModel::predict — Predict a value for previously unseen data
- SVMModel::save — Save a model to a file

The SVM class

Introduction

Class synopsis

SVM

class SVM {

/* Constants */

const integer SVM::C_SVC = 0 ;

const integer SVM::NU_SVC = 1 ;

const integer SVM::ONE_CLASS = 2 ;

const integer SVM::EPSILON_SVR = 3 ;

const integer SVM::NU_SVR = 4 ;

const integer SVM::KERNEL_LINEAR = 0 ;

const integer SVM::KERNEL_POLY = 1 ;

const integer SVM::KERNEL_RBF = 2 ;

const integer SVM::KERNEL_SIGMOID = 3 ;

const integer SVM::KERNEL_PRECOMPUTED = 4 ;

const integer SVM::OPT_TYPE = 101 ;

const integer SVM::OPT_KERNEL_TYPE = 102 ;

const integer SVM::OPT_DEGREE = 103 ;

const integer SVM::OPT_SHRINKING = 104 ;

const integer SVM::OPT_PROPABILITY = 105 ;

const integer SVM::OPT_GAMMA = 201 ;

const integer SVM::OPT_NU = 202 ;

const integer SVM::OPT_EPS = 203 ;

const integer SVM::OPT_P = 204 ;

const integer SVM::OPT_COEF_ZERO = 205 ;

const integer SVM::OPT_C = 206 ;

const integer SVM::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 ] )

}

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

The SVMModel class

Introduction

The SVMModel is the end result of the training process. It can be used to classify previously unseen data.

Class synopsis

SVMModel

class SVMModel {

/* Methods */

public bool checkProbabilityModel ( void )

public __construct ([ string $filename ] )

public array getLabels ( void )

public int getNrClass ( void )

public int getSvmType ( void )

public float getSvrProbability ( void )

public bool load ( string $filename )

public float predict_probability ( array $data )

public float predict ( array $data )

public bool save ( string $filename )

}