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Matlabでlibsvmを使用する方法は？

私はmatlabを初めて使用し、libsvmの使用方法がわかりません。 SVMでいくつかのデータ（2つの機能を含む）を分類し、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、およびシグモイド）はどうですか？ libsvmパッケージでそのreadmeファイルを見ましたが、その先頭または末尾を作成できませんでした。matlabでサポートベクターマシン（SVM）を使用した2つのクラスの分類の例を教えてください。

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

どんな助けでも大歓迎です。

matlabkernelmachine-learningsvmlibsvm
9
Sina

Libsvmパッケージのmatlab/READMEファイルには、次の例があります。

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.
12
Oli