Application of Bayesian MLP Techniques to Predicting Mineralization Potential from Geoscientific Data

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Abstract

Conventional neural network training methods attempt to find a single set of values for the network weights by minimizing an error function using a gradient descent based technique. In contrast, the Bayesian approach estimates the posterior distribution of weights, and produces predictions by integrating over this distribution. A distinct advantage of the Bayesian approach is that the optimization of parameters such as weight decay regularization coefficients can be performed without use of a cross-validation procedure. In the context of mineral potential mapping, this leads to maps which display far less variability than maps produced using conventional MLP training techniques, the latter which are highly sensitive to factors such as initial weights and cross-validation partitioning.