Whats new in version 3.18:• if class labels are 1 and -1, ensure labels[0] = 1 and labels[1] = -1
• initialize model->sv_indices as null in svm_load_model
• if nr_fold > # data, change nr_fold to be # data and ro leave-one-out cv
matlab interface:
• handle the problem where output variables are not specified
Publisher review:LIBSVM is an integrated software for support vector classification, (C-SVC, nu-SVC), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM). It supports multi-class classification.
LIBSVM provides a simple interface where users can easily link it with their own programs.
The application offers different SVM formulations, efficient multi-class classification, cross validation for model selection, probability estimates, various kernels (including precomputed kernel matrix), weighted SVM for unbalanced data and much more.
Operating system:Mac OS X
LIBSVM 3.18 details 
