An Evaluation of Confidence Bound Estimation Methods for Neural Networks
نویسندگان
چکیده
When artifical neural networks (ANN) are used in the prediction problems, it is usually desirable that some form of confidence bound is placed on the predicted value. Methods to estimate the confidence bound are available. However, these methods are valid under certain assumptions, which are rarely satisfied in practice. The behavior of the estimated confidence bound are not well understood when the assumptions are violated. We have designed some test functions to examine the behavior, and suggest how the estimated confidence bound can be corrected. The suggested method is used in the prediction of rock porosity values from seismic data for oil reservoir characterisation.
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