问题描述
我是Matlab的新手，不知道如何使用libsvm.是否有用于通过SVM对某些数据(具有2个功能)进行分类，然后可视化结果的示例代码?用内核(RBF，多项式和Sigmoid)怎么样?我在libsvm包中看到了该自述文件，但是我无法说明它的头还是尾，请您给出一个使用matlab中的支持向量机(SVM)对2类进行分类的示例，例如:
I am new to matlab and don't know how to use libsvm. Is there any sample code for classifying some data (with 2 features) with a SVM and then visualize the result? How about with kernel (RBF, Polynomial, and Sigmoid )?I saw that readme file in libsvm package, but I could not make a head or tail of it would you please give an example of classification of 2 classes using Support Vector Machines (SVM) in matlab something like:
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.
推荐答案
在libsvm软件包的matlab/README文件中，您可以找到以下示例:
In libsvm package, in the file matlab/README, you can find the following examples:
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.
这篇关于如何在Matlab中使用libsvm?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！