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Toolbox SimpleSVM Documentation

Gaëlle Loosli

Abstract

Here is a quick guide for SimpleSVM toolbox for Matlab. It contains

a quick start tour as well as some details about special features. We also

give an overview of the algorithm.

1 How to use it

1.1 Installation and test

After downloading the archive, you just need to unzip it where it is the most

convenient for you. Then you add your repertory in your matlab path (

'addpath

('/home/.../simpleSVM/')'

). You're done!

To check how well it works, you can launch

'graphicalInterface'

if you are

running Matlab 7, or

'graphicalInterface_v6'

if you are running a previous

version (I didn't check for Matlab 5 but it should work). You can also launch

'demo_online'

. This little program will execute a serie of examples that uses

the dierent features of the toolbox.

1.2 Quick start tour

Here we give a code sample that uses the toolbox the simplest way. It computes

the binary SVM solution for a checkers problem.

global svModel

[x,y,xt,yt]=dataset('Checkers',200,50,0.5); % generates data

donnees = data(x,y,xt,yt); % stores data

noyau = kernel('rbf',.9); % stores kernel

parametres = param(500,50,'binary','chol'); % stores parameters

trainSVM(donnees, noyau, parametres); % train the SVM

prediction = testSVM; % gives the results

% on the test set

1.3 Description of structures and main functions

This toolbox uses ve structures to store the data, parameters and results.

The main one is a global variable called

svModel

that should be declared in

any program using the toolbox. It was made as a global variable for memory

purpose. Matlab makes a copy of every input parameter called for a function

which is a problem for large datasets. The global variable prevents it, but you

need to be careful about it.

This structure contains four structures:

1

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