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Author Carney, John ♦ Cunningham, Padraig ♦ Bhagwan, Urmesh ♦ England, London
Source CiteSeerX
Content type Text
File Format PDF
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Prediction Interval ♦ Model Uncertainty ♦ Accurate Confidence ♦ Neural Network Ensemble ♦ True Regression ♦ Specific Ensemble Technique ♦ Neural Network ♦ Preliminary Result ♦ Reflect Th ♦ New Technique ♦ Training Set ♦ Ensemble Technique
Description IEEE International Joint Conference on Neural Networks
In this paper we propose a new technique that uses the bootstrap to estimate confidence and prediction intervals for neural network (regression) ensembles. Our proposed technique can be applied to any ensemble technique that uses the bootstrap to generate the training sets for the ensemble, such as bagging [1] and balancing [5]. Confidence and prediction intervals are estimated that include a significantly improved estimate of underlying model uncertainty (i.e.) the uncertainty of our estimate of the "true" regression. Unlike existing techniques, this estimate of uncertainty will vary according to which ensemble technique is used -- if the effect of using a specific ensemble technique is to produce less model uncertainty than using another ensemble technique, then this will be reflected in the confidence and prediction intervals. Preliminary results illustrate how our technique can provide more accurate confidence and prediction intervals (intervals that better reflect th...
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article
Publisher Date 1999-01-01