On Approximate Solutions to Support Vector Machines
Dongwei Cao, Daniel L. Boley · 2006
We propose to speed up the training process of support vector machines (SVM) by resorting to an approximate SVM, where a small number of representatives are extracted from the original training data set and used for training. Theoretical studies show that, in order for the approximate SVM to be similar to the exact SVM given by the original training data set, kernel k-means should be used to extract the representatives. As practical variations, we also propose two efficient implementations of the proposed algorithm, where approximations to kernel k-means are used. The proposed algorithms are compared against the standard training algorithm over real data sets.