matlab打开README文件，有答案。我的是libsvm3.22。每个版本不一样的

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.