Confidence interval prediction for neural network models
By George Chryssolouris, Moshin Lee, Alvin Ramsey. 1996
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Travers Ching
- Alexandr A Kalinin
- Brett K. Beaulieu-Jones
- Daniel S. Himmelstein
- Hao Quan
- Amr M. Alexandari
- Dipti Srinivasan
- G. Way
Citations in the corpus
- Opportunities and obstacles for deep learning in biology and medicine (2017, background): “[359] provided confidence intervals under the assumption of normally-distributed error.”
- Application of soft computing techniques in machining performance prediction and optimization: a literature review (2010, background): “Therehavebeenveryfew attemptsinthisdirection.Some authors have developed neural network models that can predict the confidence intervals [ 140 , 141 ]o f the prediction.”
- Quantification of the predictive uncertainty of artificial neural network based river flow forecast models (2012, methodology): “The delta technique introduced by Chryssolouris et al. (1996) considers linearising the ANN model around a set of parameters, and constructing the prediction interval by application of standard asymptotic theory to the linearized model.”
- Short-Term Load and Wind Power Forecasting Using Neural Network-Based Prediction Intervals (2014, background): “Implementation difficulties, special assumptions about the data distribution, and massive computational requirements [24]–[26] hinder widespread applications of these methods for decision making.”
- Lower Upper Bound Estimation Method for Construction of Neural Network-Based Prediction Intervals (2011, methodology): “[14] and Hwang and Ding [15] developed the delta technique through the nonlinear regression representation of the NNs.”
- Avoiding Pitfalls in Neural Network Research (2007, background): “The lack of transparency, thus, contributes to errors in published research work.”