In this paper we propose a Monte Carlo-based learning algorithm for multi-layer perceptron (MLP) which is characterized by the following features: first, the learning algorithm is able to associate to each weight of the MLP a probability distribution converging in distribution to the standardized normal one; then, the Monte Carlo-based learning algorithm performs a global search in the space of the weights.

A proposal for a Monte Carlo-based learning algorithm for multi-layer perceptron

CORAZZA, Marco
2004-01-01

Abstract

In this paper we propose a Monte Carlo-based learning algorithm for multi-layer perceptron (MLP) which is characterized by the following features: first, the learning algorithm is able to associate to each weight of the MLP a probability distribution converging in distribution to the standardized normal one; then, the Monte Carlo-based learning algorithm performs a global search in the space of the weights.
2004
Atti del Convegno "Metodi Matematici e Statistici per l'Analisi dei Dati Assicurativi e Finanziari"
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10278/26623
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