Matlab'da libsvm nasıl kullanılır?

Question

Matlab için yeni ve libsvm'yi nasıl kullanacağımı bilmiyorum. SVM ile bazı verileri (2 özellikli) sınıflandırmak ve sonucu görselleştirmek için örnek kod var mı? Çekirdeğe (RBF, Polinom ve Sigmoid) ne dersiniz? Ben LIBSVM paketinde o beni oku dosyasını gördüm, ama sizin gibi matlab şey Destek Vektör Makineleri (SVM) kullanılarak 2 sınıfların sınıflandırma Bir örnek vermek misiniz kafa veya onun kuyruk gelemedi:

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 

Herhangi bir yardım son derece takdir edilecektir. LIBSVM pakette

Answer 1

, dosya matlab/README, aşağıdaki örnek bulabilirsiniz:

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.