(Matlab)SVM工具箱快速入手简易教程

1、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, R

2、BFSigmaValue, .)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 = svmtr

3、ain(., BoxConstraint, BoxConstraintValue, .)SVMStruct = svmtrain(., Autoscale, AutoscaleValue, .)SVMStruct = svmtrain(., Showplot, ShowplotValue, .)-svmclassifyClassify data using support vector machineSyntaxGroup = svmclassify(SVMStruct, Sample)Group = svmclassify(SVMStruct, Sample, Showplot, Showp

4、lotValue)=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:tu11.jpg (7.94 KB)2009-5-12 19:50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,versico

5、lor,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:tu22.jpg (26

6、.86 KB)2009-5-12 19:50classes = svmclassify(svmStruct,data(test,:),showplot,true);%对于未知的测试集进行分类预测,结果如图:tu33.jpg (37.34 KB)2009-5-12 19:50classperf(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: libsvm-mat-2.86-1.rar (73.75 KB) libsvm-mat

7、-2.86-1.rar (73.75 KB)下载次数: 3732009-5-12 20:02安装方法也很简单,解压文件,把当前工作目录调整到libsvm所在的文件夹下,再在set path里将libsvm所在的文件夹加到里面.然后在命令行里输入mex -setup%选择一下编译器make 这样就可以了.建议大家使用libsvm工具箱,这个更好用一些.可以进行分类多类别,预测.=svmtrainsvmpredict=简要语法:Usage=matlab model = svmtrain(training_label_vector, training_instance_matrix , libsvm

8、_options); -training_label_vector: An m by 1 vector of training labels (type must be double). -training_instance_matrix: An m by n matrix of m training instances with n features. It can be dense or sparse (type must be double). -libsvm_options: A string of training options in the same format as that

9、 of LIBSVM.matlab predicted_label, accuracy, decision_values/prob_estimates = svmpredict(testing_label_vector, testing_instance_matrix, model , libsvm_options); -testing_label_vector: An m by 1 vector of prediction labels. If labels of test data are unknown, simply use any random values. (type must

10、be double) -testing_instance_matrix: An m by n matrix of m testing instances with n features. It can be dense or sparse. (type must be double) -model: The output of svmtrain. -libsvm_options: A string of testing options in the same format as that of LIBSVM.Returned Model Structure=实例研究:load heart_sc

11、ale.mat%工具箱里自带的数据如图:tu44.jpg (9.36 KB)2009-5-12 20:08其中 heart_scale_inst是样本,heart_scale_label是样本标签model = svmtrain(heart_scale_label, heart_scale_inst, -c 1 -g 0.07);%训练样本,具体参数的调整请看帮助文件predict_label, accuracy, dec_values = svmpredict(heart_scale_label, heart_scale_inst, model);%分类预测,这里把训练集当作测试集,验证效果

12、如下: predict_label, accuracy, dec_values = svmpredict(heart_scale_label, heart_scale_inst, model); % test the training dataAccuracy = 86.6667% (234/270) (classification)=这回把SVM这点入门的东西都说完了,大家可以参照着上手了,有关SVM的原理我下面有个简易的PPT,是以前做项目时我做的当时我负责有关SVM这一块代码实现讲解什么的,感兴趣的你可以看看,都是上手较快的东西,想要深入学习SVM,你的学习统计学习理论什么的.挺多的呢.SVM.ppt (391 KB) SVM.ppt (391 KB)下载次数: 4292009-5-12 20:18-有关SVM和libsvm的非常好的资料,想要详细研究SVM看这个-libsvm_guide.p