Selección de perceptrones multicapa usando aprendizaje bayesiano
DOI:
https://doi.org/10.22517/23447214.1489Abstract
La Regularización Bayesiana de perceptrones multicapa pretende resolver el problema de optimización de los pesos de la red neuronal simultáneamente con el problema de generalización. En este trabajo se realiza un análisis de la regularización Bayesiana, que parece ser una de las más poderosas técnicas de entrenamiento de perceptrones multicapa, para luego hacer un comparativo con los resultados obtenidos usando Regla Delta Generalizada. Finalmente se discute alguna implicación de los resultados obtenidos respecto a la técnica basada en algoritmos constructivos para la selección final de neuronas en la capa oculta.Downloads
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