(Matlab)SVM工具箱快速入手简易教程.ppt

SVM工具箱快速入手简易教程(by
faruto)
一. matlab 自带的函数(matlab帮助文件里的例
子)[只有较新版本的 matlab中有这两个 SVM的函数]
=====
svmtrain svmclassify
=====简要语法规则====
svmtrain
Train support vector machine classifier
Syntax
SVMStruct = svmtrain(Training, Group)
SVMStruct = svmtrain(..., 'Kernel_Function', Kernel_FunctionValue, ...)
SVMStruct = svmtrain(..., 'RBF_Sigma', RBFSigmaValue, ...)
SVMStruct = svmtrain(..., 'Polyorder', PolyorderValue, ...)
SVMStruct = svmtrain(..., 'Mlp_Params', Mlp_ParamsValue, ...)
SVMStruct = svmtrain(..., 'Method', MethodValue, ...)
SVMStruct = svmtrain(..., 'QuadProg_Opts', QuadProg_OptsValue, ...)
SVMStruct = svmtrain(..., 'SMO_Opts', SMO_OptsValue, ...)
SVMStruct = svmtrain(..., 'BoxConstraint', BoxConstraintValue, ...)
SVMStruct = svmtrain(..., 'Autoscale', AutoscaleValue, ...)
SVMStruct = svmtrain(..., 'Showplot', ShowplotValue, ...)
---------------------
svmclassify
Classify data using support vector machine
Syntax
Group = svmclassify(SVMStruct, Sample)
Group = svmclassify(SVMStruct, Sample, 'Showplot', ShowplotValue)
============================实例研究====================
load fisheriris
%载入 matlab自带的数据[有关数据的信息可以自己到 UCI查找,这是 UCI的经典
数据之一],得到的数据如下图:
tu1
其中meas是150*4的矩阵代表着有150个样本每个样本有4个属性描述,species
代表着这 150个样本的分类.
data = [meas(:,1), meas(:,2)];
%在这里只取 meas的第一列和第二列,即只选取前两个属性.
groups = ismember(species,'setosa');
%由于 species分类中是有三个分类:setosa,versicolor,virginica,为了