LIBSVM-Demo-Applet

Chih-Chung Chang and Chih-Jen Lin

Here is a simple applet demonstrating SVM classification and regression.
Click on the drawing area and use ``Change'' to change class of data. Then use ``Run'' to see the results.

Examples of options: -s 0 -c 1000 -t 1 -g 1 -r 1 -d 3
Classify a binary data with polynomial kernel (u'v+1)^3 and C = 1000

 
options:
-s svm_type : set type of SVM (default 0)
	0 -- C-SVC
	1 -- nu-SVC
	2 -- one-class SVM
	3 -- epsilon-SVR
	4 -- nu-SVR
-t kernel_type : set type of kernel function (default 2)
	0 -- linear: u'*v
	1 -- polynomial: (gamma*u'*v + coef0)^degree
	2 -- radial basis function: exp(-gamma*|u-v|^2)
	3 -- sigmoid: tanh(gamma*u'*v + coef0)
-d degree : set degree in kernel function (default 3)
-g gamma : set gamma in kernel function (default 1/k)
-r coef0 : set coef0 in kernel function (default 0)
-c cost : set the parameter C of C-SVC, epsilon-SVR, and nu-SVR (default 1)
-n nu : set the parameter nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5)
-p epsilon : set the epsilon in loss function of epsilon-SVR (default 0.1)
-m cachesize : set cache memory size in MB (default 40)
-e epsilon : set tolerance of termination criterion (default 0.001)
-h shrinking: whether to use the shrinking heuristics, 0 or 1 (default 1)
-wi weight: set the parameter C of class i to weight*C in C-SVC (default 1)
-v n: n-fold cross validation mode

The k in the -g option means the number of attributes in the input data.

option -v randomly splits the data into n parts and calculates cross
validation accuracy/mean squared error on them.