我是Matlab的新手，不知道如何使用libsvm。是否有示例代码可以用来对具有两个特征的数据进行分类，并使用支持向量机（SVM）然后可视化结果？使用核函数（RBF、多项式和Sigmoid）的情况如何？我看到了libsvm包中的readme文件，但我无法理解其中的内容。您能否提供一个在Matlab中使用支持向量机（SVM）对两个类别进行分类的示例，像这样：
Attribute_1 Attribute_2 Class170 66 -1160 50 -1170 63 -1173 61 -1168 58 -1184 88 +1189 94 +1185 88 +1任何帮助都将不胜感激。
回答：
在libsvm包中，在文件matlab/README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 dataFor 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 columnmatlab> model = svmtrain(label_vector, K1, '-t 4');matlab> [predict_label, accuracy, dec_values] = svmpredict(label_vector, K1, model); % test the training dataWe 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 Datamatlab> 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 Kernelmatlab> 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 Kernelmatlab> 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 kernelmatlab> accuracy_P % Display the accuracy using precomputed kernelNote 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.