Trainable classifier: LIBSVM, nu-algorithme
[W,J,NU] = NULIBSVC(A,KERNEL,NU)
Optimises a support vector classifier for the dataset A by the libsvm package, see http://www.csie.ntu.edu.tw/~cjlin/libsvm/. LIBSVC calls the svmtrain routine of libsvm for training. Classifier execution for a test dataset B may be done by D = B*W; In D posterior probabilities are given as computed by svmpredict using the '-b 1' option.
The kernel may be supplied in KERNEL by
If KERNEL = 0 (or not given) it is assumed that A is already the kernelmatrix (square). In this also a kernel matrix should be supplied at evaluation by B*W or PRMAP(B,W). However, the kernel has to be computed with respect to support objects listed in J (the order of objects in J does matter).
R.-E. Fan, P.-H. Chen, and C.-J. Lin. Working set selection using the second order information for training SVM. Journal of Machine Learning Research 6, 1889-1918, 2005