(Matab)SVM工具箱快速入手简易教程.doc

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====svmtrainTrain support vector machine classifierSyntaxSVMStruct = 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, ...)---------------------svmclassifyClassify data using support vector machineSyntaxGroup = 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,为了使问题简单,我们将其变为二分类问题:Setosa and non-Setosa.[train, test] = crossvalind(holdOut,groups);cp = classperf(groups);%随机选择训练集合测试集[有关crossvalind的使用请自己help一下]其中cp作用是后来用来评价分类器的.svmStruct = svmtrain(data(train,:),groups(train),showplot,true);%使用svmtrain进行训练,得到训练后的结构svmStruct,在预测时使用.训练结果如图:tu2 classes = svmclassify(svmStruct,data(test,:),showplot,true);%对于未知的测试集进行分类预测,结果如图:tu3 classperf(cp,classes,test);cp.CorrectRateans =? ? 0.9867%分类器效果测评,就是看测试集分类的准确率的高低.二.台湾林智仁的libsvm工具箱该工具箱下载[libsvm-mat-2.86-1]: HYPERLINK /attachment.php?aid=24867k=c05691426227073bc1909cb597eceacdt=1263976450 \t _blank libsvm-mat-2.86-1.rar (73.7