%A Francisco J. Gonzalez-Castano and Robert R. Meyer
%T Chunking-Synthetic Approaches to Large-Scale Kernel Machines
%D October 2000
%R 00-04
%I COMPUTER SCIENCES DEPARTMENT, UNIVERSITY OF WISCONSIN
%C MADISON, WI
%X
We consider a kernel-based approach to nonlinear classification that combines the generation of
"synthetic" points (to be used in the kernel) with "chunking" (working with subsets of the
data) in order to significantly reduce the size of the optimization problems required to
construct classifiers for massive datasets. Rather than solving a single massive classification
problem involving all points in the training set, we employ a series of problems that gradually
increase in size and which consider kernels based on small numbers of synthetic points. These
synthetic points are generated by solving relatively small nonlinear unconstrained optimization
problems. In addition to greatly reducing optimization problem size, the procedure that we
describe also has the advantage of being easily parallelized. Computational results show that
our method efficiently generates high-performance classifiers on a variety of problems
involving both real and randomly generated datasets.