A fast and compact classifier based on sorting in an iteratively expanded input space

Abstract

This paper proposes a compact neural classifier, based on the theory of simplicial decomposition and approximation, with a very convenient hardware or software implementation. It can learn arbitrary n-inputs patterns with O(n) time complexity. There are no multipliers required, and the learned knowledge is stored in a general purpose RAM with a size ranging from O(n) to O(n2). The proposed architecture is composed only of four building blocks, an iterative nonlinear expander, a sorter, a RAM memory, and an accumulator, all of them readily available in either digital hardware or software technology. Simulation results indicate good accuracy for a wide variety of benchmark problems. © 2008 Wiley Periodicals, Inc.