Scaling Up Support Vector Machines using Nearest Neighbor Condensation

Abstract--In this brief, we describe the FCNN-SVM classifier, which
combines the support vector machine (SVM) approach and the fast
nearest neighbor condensation classification rule (FCNN) in order to make
SVMs practical on large collections of data. As a main contribution, it
is experimentally shown that, on very large and multidimensional data
sets, the FCNN-SVM is one or two orders of magnitude faster than SVM,
and that the number of support vectors (SVs) is more than halved with
respect to SVM. Thus, a drastic reduction of both training and testing
time is achieved by using the FCNN-SVM. This result is obtained at the
expense of a little loss of accuracy. The FCNN-SVM is proposed as a viable
alternative to the standard SVM in applications where a fast response time
is a fundamental requirement.