What's the difference between LibSVM and LibLinear
algorithm, libsvm, machine-learning, svm
Solution
In practice the complexity of the SMO algorithm (that works both for kernel and linear SVM) as implemented in libsvm is O(n^2) or O(n^3) whereas liblinear is O(n) but does not support kernel SVMs. n is the number of samples in the training dataset.
Hence for medium to large scale forget about kernels and use liblinear (or maybe have a look at approximate kernel SVM solvers such as LaSVM).
Edit: in practice libsvm becomes painfully slow at 10k samples.
Problem
libsvm and liblinear are both software libraries that implement Support Vector Machines. What's the difference? And how do the differences make liblinear faster than libsvm?