Journal Title
Title of Journal: NCA
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Abbravation: Neural Computing & Applications
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Publisher
Springer-Verlag
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Authors: D Lowe C Zapart
Publish Date: 2014/03/03
Volume: 8, Issue: 1, Pages: 77-85
Abstract
In developing neural network techniques for real world applications it is still very rare to see estimates of confidence placed on the neural network predictions This is a major deficiency especially in safetycritical systems In this paper we explore three distinct methods of producing pointwise confidence intervals using neural networks We compare and contrast Bayesian Gaussian Process and Predictive error bars evaluated on real data The problem domain is concerned with the calibration of a real automotive engine management system for both airfuel ratio determination and online ignition timing This problem requires realtime control and is a good candidate for exploring the use of confidence predictions due to its safetycritical nature
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