NeuroCOLT

Neural Networks and Computational Learning Theory

 

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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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