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NeuroCOLT
Technical Report NC-TR-95-020
An
incremental neural classifier on a MIMD computer
Arnulfo
Azcarraga
LIFIA - IMAG - INPG, France
Helene
Paugam-Moisy and Didier Puzenat
LIP - URA 1398 du CNRS
ENS Lyon, France
Abstract
MIMD computers are among the best parallel architectures available.
They are easily scalable with numerous processors and have potentially
huge comput ing power. One area of application for such computers
is the field of neural net works. This article presents a study, and
two parallel implementations, of a spe cific neural incremental classifier
of visual patterns. This neural network is i ncremental in that network
units are created whenever the classifier is not able to recognize
correctly a pattern. The dynamic nature of the model renders the p
arallel algorithms rather complex.
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