(Matlab)SVM工具箱快速入手簡(jiǎn)易教程

1、(Matlab)SVM工具箱快速入手簡(jiǎn) 易教程SVM工具箱快速入手簡(jiǎn)易教程(byfaruto)一 . matlab 自帶的函數(shù) (matlab 幫助文件里的例子)只有較新版本的matlab中有這兩個(gè)SVM的函數(shù)svmtrain svmclassify=簡(jiǎn)要語(yǔ)法規(guī)則 =svmtrainTrain support vector machine classifierSyntaxSVMStruct = svmtrain(Training, Group)SVMStruct = svmtrain(.,Kernel_Function, Kernel_FunctionValue, .)SVMStruct =

2、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_OptsVa

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

4、Sample, Showplot, ShowplotValue)=實(shí)=例研究 = load fisheriris賊入matlab自帶的數(shù)據(jù)有關(guān)數(shù)據(jù)的信息可以自己到 UCI查找,這是UCI的經(jīng) 典數(shù)據(jù)之一 , 得到的數(shù)據(jù)如下圖 :tu1NdmeValueMinH- 回 rmeas species150x4 doubles model = svmtrain(training_label_vector, training_instance_matrix , libsvm_options);-training_label_vector:An mby 1 vector of training label

5、s (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 trainingoptions in the same format as that of LIBSVM.matlab predicted_label, accuracy, decision_values/prob_estimates

6、= svmpredict(testing_label_vector, testing_instance_matrix, model , libsvm_options);-testing_label_vector:An m by 1 vector of prediction labels. If labels of testdata are unknown, simplyuse any random values. (type must be double) -testing_instance_matrix:An mby n matrix of mtesting instances with n

7、 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實(shí)例研究 :load heart_scale.mat%工具箱里自帶的數(shù)據(jù)如圖:tu4NameVlueFR heart_scakjn5t 270x13 doubfe?1 heart sal label -其中 hea

8、rt_scale_i nst是樣本,heart_scale_label是樣本標(biāo)簽model = svmtrai n(heart_scaleabel, heart_scale_i nst, -c 1 -g 0.07);%訓(xùn)練樣本，具體參數(shù)的調(diào)整請(qǐng)看幫助文件predict_label, accuracy, dec_values= svmpredict(heart_scaleabel, heart_scale_i nst, model);%分類預(yù)測(cè)，這里把訓(xùn)練集當(dāng)作測(cè)試集，驗(yàn)證效果如下 predict_label, accuracy, dec_values= svmpredict(heart_scaleabel, heart_scale_i nst, model); % test the training dataAccuracy = 86.6667% (234/270) (classificatio n) 這回把SVM這點(diǎn)入門(mén)的東西都說(shuō)完了 ,大家可以參照 著上手了 ,有關(guān)SVM的原理我下面有個(gè)簡(jiǎn)易的PPT，是 以前做項(xiàng)目時(shí)我做的當(dāng)時(shí)我負(fù)責(zé)有關(guān)SVM這一塊代 碼實(shí)現(xiàn)講解什么的,感興趣的你可以看看，都是上手 較快的東西,想要深入學(xué)習(xí)SVM你的學(xué)習(xí)統(tǒng)計(jì)學(xué)習(xí)理 論什么的挺多的呢.SVM.ppt (391 KB)