Although JkernelMachines is a library designed to be extended by the use of your own kernels, there are currently 2 standalone programs in JKernelMachines that can be launched out of the box.
The first one is fr.lip6.example.SVMExample which reads data from a file in libsvm format, split it in half, uses the first part for training an SVM with Gaussian kernel and the other half for evaluating the accuracy.
The second standalone program is aimed at computing accuracy using a cross-validation procedure. It is fr.lip6.example.CrossValidationExample
CrossValidationExample -f file [-p percent] [-n nbtests] [-k kernel] [-a algorithm] [-vvv] -f file: the data file in libsvm format -p percent: the percent of data to keep for training -n nbtests: the number of test to perform during crossvalidation -k kernel: the type of kernel (linear or gauss, default gauss) -a algorithm: type of SVM algorithm(lasvm, lasvmi, smo, default lasvm) -v: verbose (v few, vv lot, vvv insane, default none)
For example, you can run
java -cp jkernelmachines.jar fr.lip6.example.CrossValidationExample -f ionosphere_scale -p 0.7 -n 20 -k gaussl2 -a lasvmi
This assumes your data file is
ionosphere_scale. 70% of data are kept for training. 20 random split are done for the crossvalidation. a Gaussian kernel is used with LaSVM-I algorithm.
The output should be something like
Accuracy: 0.8990566037735848 +/- 0.02878518170262708
You can find data in libsvm format here