LibSVM and LibLINEAR

Ian Witten demonstrates LibLINEAR, which contains fast algorithms for linear classification; and LibSVM, which produces non-linear SVMs. Both implement support vector machines – which are already available in Weka as the SMO method. The difference is that LibLINEAR is generally far faster than SMO (and can, optionally, minimize the sum of absolute values of errors instead of the sum of squared errors), while LibSVM is far more flexible. Support vector machines can be made to implement different kinds of non-linear decision boundaries using different kernels, and the effect can be explored using Weka’s boundary visualizer. They benefit greatly from a parameter optimization process, which can be done using Weka’s gridSearch meta-classifier.