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Confidence estimation methods for neural networks : a practical comparison

14 years 25 days ago
Confidence estimation methods for neural networks : a practical comparison
Feed-forward neural networks (Multi-Layered Perceptrons) are used widely in real-world regression or classification tasks. A reliable and practical measure of prediction "confidence" is essential in real-world tasks. This paper compares three approachesto prediction confidenceestimation, using both artificial and real data. The three methods are maximum likelihood, approximate Bayesian and bootstrap. Both noiseinherent to the dataand model uncertaintyare considered.
Georgios Papadopoulos, Peter J. Edwards, Alan F. M
Added 01 Nov 2010
Updated 01 Nov 2010
Type Conference
Year 2000
Where ESANN
Authors Georgios Papadopoulos, Peter J. Edwards, Alan F. Murray
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