Una revisión de redes MLP como clasificadores de múltiples clases
A survey on MLP neural networks as multi-class classifiers
DOI:
https://doi.org/10.54167/tch.v9i3.587Palabras clave:
clasificador múltiples clases MLP, red neuronal, entrenamiento MLP, aplicación clasificadoresResumen
Se presenta el estado actual de clasificadores de múltiples clases implementados con redes Multi Layer Perceptron, MLP. Los clasificadores de múltiples clases basados en redes MLP han sido utilizados en muchos casos con éxito. Se presentan, primero, los aspectos generales y las diferentes formas de implementar clasificadores de múltiples clases, incluyendo las redes MLP. Después se presentan aspectos de arquitectura de las redes MLP clasificadoras incluyendo consideraciones de diseño y organización tales como: capas de entrada, ocultas y de salida, así como la cantidad de neuronas en cada capa. Luego viene una revisión acerca de las metodologías existentes para su entrenamiento, y cómo es que la organización de la red afecta las condiciones de entrenamiento. A continuación, se presentan casos de uso de las redes MLP como clasificadores, sus características y detalles acerca de los parámetros referentes al diseño de la red y también se revisan los resultados de su aplicación. En el material revisado, pareciera ser que el desempeño depende en gran medida de su aplicación específica, aunque no existe trabajo que demuestre esto en forma determinante.
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