我是 matlab 新手，不知道如何使用 libsvm。是否有任何示例代码用于使用 SVM 对某些数据（具有 2 个特征）进行分类，然后将结果可视化？内核（RBF、多项式和 Sigmoid）怎么样？我在 libsvm 包中看到了该自述文件，但我无法确定它的开头或结尾，请您举一个在 matlab 中使用支持向量机 (SVM) 对 2 个类进行分类的示例，例如：
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
任何帮助将不胜感激。
在 libsvm 包的 matlab/README 文件中，您可以找到以下示例：
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.