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A fast and compact classifier based on sorting in an iteratively expanded input space
Author(s) -
Dogaru Radu,
Glesner Manfred
Publication year - 2008
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.20286
Subject(s) - computer science , classifier (uml) , accumulator (cryptography) , software , ranging , computer engineering , artificial intelligence , benchmark (surveying) , architecture , algorithm , sorting , pattern recognition (psychology) , theoretical computer science , parallel computing , machine learning , programming language , art , telecommunications , geodesy , visual arts , geography
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 ( n 2 ). 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.