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In this paper we propose a Monte Carlo-based learning algorithm which is able to associate to the estimator of each weight of a given multi-layer perceptron a probability distribution converging in distribution to the standardized normal one. Moreover, the learning algorithm performs a global search in the space of the weights.

Multi-layer perceptron learning via Monte Carlo approach: A proposal

CORAZZA, Marco
2004-01-01

Abstract

In this paper we propose a Monte Carlo-based learning algorithm which is able to associate to the estimator of each weight of a given multi-layer perceptron a probability distribution converging in distribution to the standardized normal one. Moreover, the learning algorithm performs a global search in the space of the weights.
2004
2004
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10278/14837
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