An Empirical Study of the Influence of Data Structures on the Performance of VG-RAM Classifiers

Author

Alves, Daniel S. F. ; Cardoso, Douglas O. ; Carneiro, Hugo C. C. ; Franca, Felipe M. G. ; Lima, Priscila M. V.

Author_Institution

PESC - COPPE, Univ. Fed. do Rio de Janeiro, Rio de Janeiro, Brazil

fYear

2013

fDate

8-11 Sept. 2013

Firstpage

388

Lastpage

393

Abstract

This work investigates the effect of different data structures on the performance and accuracy of VG-RAM-based classifiers. This weightless neural model is based on RAM nodes having very large address input, what suggests the use of special data structures in order to deal with space and time computational costs. Four different data structures are explored, including the classical one used in recent VG-RAM related literature, resulting in a novel and accurate yet fast setup.

Keywords

data structures; neural nets; pattern classification; random-access storage; RAM nodes; VG-RAM classifier performance; data structures; space computational costs; time computational costs; virtual generalizing random access memory; weightless neural model; Accuracy; Biological neural networks; Data structures; Neurons; Random access memory; Time complexity; Training; VG-RAM; data structures; weightless neural networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence and 11th Brazilian Congress on Computational Intelligence (BRICS-CCI & CBIC), 2013 BRICS Congress on

Conference_Location

Ipojuca

Type

conf

DOI

10.1109/BRICS-CCI-CBIC.2013.71

Filename

6855880