Designing Optimized Pattern Recognition
Systems By Learning Voronoi Vectors Using
Genetic Algorithms
Author(s)
Claudio M.N.A. Pereira and Roberto Schirru
Abstract
Designing optimized pattern recognition
systems by learning Voronoi vectors using
genetic algorithms
Claudio M. N. A. Pereira ^ and Roberto Schirru *
' Institute de Engenharia Nuclear, CREA,
Comissdo National de Energia Nuclear, Brazil
^ Programa de Engenharia Nuclear,
Universidade Federal do Rio de Janeiro, Brazil.
* Computer Science Department, Universidade Igua$u, Brazil.
Abstract
In this work is described a methodology for developing optimized pattern
recognition systems by means of genetic machine learning. The idea is to
redefine the set of classes that must be learned by the classification system,
mapping them into another set which the Voronoi vectors can successfully
classify a sample into one of the original classes. The main objective of this
approach is to find the minimum number of classification rules (by minimizing
the number of classes in the new set) that maximizes the number of correct
classifications. To accomplish that, a genetic algorithm was design. In
Keywords
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