Matlabでlibsvmを使用するには？

Question

私はmatlabを初めて使い、libsvmの使い方を知らない。 SVMでいくつかのデータ（2つの機能を持つ）を分類し、その結果を視覚化するためのサンプルコードはありますか？カーネル（RBF、多項式、シグモイド）についてはどうですか？ 私はLIBSVMパッケージでそのreadmeファイルを見ましたが、あなたのようなMATLAB何かにサポートベクターマシン（SVM）を使用して、2クラスの分類の例をお願いします、私はそれの頭や尾を作ることができなかった。

Attribute_1 Attribute_2 Class 
170   66   -1 
160   50   -1 
170   63   -1 
173   61   -1 
168   58   -1 
184   88   +1 
189   94   +1 
185   88   +1 

ご迷惑をおかけして申し訳ございません。 LIBSVMパッケージで

Answer 1

、ファイルMATLAB/READMEには、次の例を見つけることができます：

Examples 
======== 

Train and test on the provided data heart_scale: 

matlab> [heart_scale_label, heart_scale_inst] = libsvmread('../heart_scale'); 
matlab> model = svmtrain(heart_scale_label, heart_scale_inst, '-c 1 -g 0.07'); 
matlab> [predict_label, accuracy, dec_values] = svmpredict(heart_scale_label, heart_scale_inst, model); % test the training data 

For probability estimates, you need '-b 1' for training and testing: 

matlab> [heart_scale_label, heart_scale_inst] = libsvmread('../heart_scale'); 
matlab> model = svmtrain(heart_scale_label, heart_scale_inst, '-c 1 -g 0.07 -b 1'); 
matlab> [heart_scale_label, heart_scale_inst] = libsvmread('../heart_scale'); 
matlab> [predict_label, accuracy, prob_estimates] = svmpredict(heart_scale_label, heart_scale_inst, model, '-b 1'); 

To use precomputed kernel, you must include sample serial number as 
the first column of the training and testing data (assume your kernel 
matrix is K, # of instances is n): 

matlab> K1 = [(1:n)', K]; % include sample serial number as first column 
matlab> model = svmtrain(label_vector, K1, '-t 4'); 
matlab> [predict_label, accuracy, dec_values] = svmpredict(label_vector, K1, model); % test the training data 

We give the following detailed example by splitting heart_scale into 
150 training and 120 testing data. Constructing a linear kernel 
matrix and then using the precomputed kernel gives exactly the same 
testing error as using the LIBSVM built-in linear kernel. 

matlab> [heart_scale_label, heart_scale_inst] = libsvmread('../heart_scale'); 
matlab> 
matlab> % Split Data 
matlab> train_data = heart_scale_inst(1:150,:); 
matlab> train_label = heart_scale_label(1:150,:); 
matlab> test_data = heart_scale_inst(151:270,:); 
matlab> test_label = heart_scale_label(151:270,:); 
matlab> 
matlab> % Linear Kernel 
matlab> model_linear = svmtrain(train_label, train_data, '-t 0'); 
matlab> [predict_label_L, accuracy_L, dec_values_L] = svmpredict(test_label, test_data, model_linear); 
matlab> 
matlab> % Precomputed Kernel 
matlab> model_precomputed = svmtrain(train_label, [(1:150)', train_data*train_data'], '-t 4'); 
matlab> [predict_label_P, accuracy_P, dec_values_P] = svmpredict(test_label, [(1:120)', test_data*train_data'], model_precomputed); 
matlab> 
matlab> accuracy_L % Display the accuracy using linear kernel 
matlab> accuracy_P % Display the accuracy using precomputed kernel 

Note that for testing, you can put anything in the 
testing_label_vector. For more details of precomputed kernels, please 
read the section ``Precomputed Kernels'' in the README of the LIBSVM 
package.