Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
An Evaluation of Confidence Bound Estimation Methods for Neural Networks
Advances in Computational Intelligence and Learning: Methods and Applications
Confidence interval prediction for neural network models
IEEE Transactions on Neural Networks
Confidence estimation methods for neural networks: a practical comparison
IEEE Transactions on Neural Networks
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The use of confidence estimation techniques on neural networks outputs plays an important role when these mathematical models are applied in many practical applications. However, few of these techniques have the capability to deal with variable noise rate in the predictions over the domain, making the assumptions about the reliability of these outputs become not suitable with their real accuracy. In this paper an extension to the non-linear regression method to estimate prediction intervals for feed forward neural networks is presented. The main idea of this method is that residuals variance should be estimated in function of the input data and not as a constant. Thus, using clustering techniques, distinct estimates of the residuals variance are made and then used to obtain new prediction intervals. Proceeding in this manner, the experiments results show that this approach can lead to prediction intervals that better reflect the confidence level of the neural network outputs.