如何在 Matlab 中使用 libsvm？

Posted 2023-03-12

【中文标题】如何在 Matlab 中使用 libsvm？

【英文标题】：How to use libsvm in Matlab?

【发布时间】：2012-01-23 07:01:39

【问题描述】：

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。

【问题讨论】：

你是否从这里使用 libsvm：csie.ntu.edu.tw/~cjlin/libsvm?

是的，我也看过那里的指南，但无法使用

【参考方案1】：

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.

【讨论】：

我知道这个线程很旧，但是关于预计算内核的“示例序列号”是什么意思？

他们指的是(1:n)'。基本上，您可以按照与样本不同的顺序提供内核。否则，只需使用(1:n)'