Communications of the ACM
Comparing the Bayes and Typicalness Frameworks
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Transductive Confidence Machines for Pattern Recognition
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Inductive Confidence Machines for Regression
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Ridge Regression Confidence Machine
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Pattern Recognition and Density Estimation under the General i.i.d. Assumption
COLT '01/EuroCOLT '01 Proceedings of the 14th Annual Conference on Computational Learning Theory and and 5th European Conference on Computational Learning Theory
Algorithmic Learning in a Random World
Algorithmic Learning in a Random World
Environmental Modelling & Software
Information Sciences: an International Journal
A non-symbolic implementation of abdominal pain estimation in childhood
Information Sciences: an International Journal
Serum Proteomic Abnormality Predating Screen Detection of Ovarian Cancer
The Computer Journal
Transduction with confidence and credibility
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Normalized nonconformity measures for regression Conformal Prediction
AIA '08 Proceedings of the 26th IASTED International Conference on Artificial Intelligence and Applications
Regression conformal prediction with nearest neighbours
Journal of Artificial Intelligence Research
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Reliable Confidence Measures for Medical Diagnosis With Evolutionary Algorithms
IEEE Transactions on Information Technology in Biomedicine
Reliable probabilistic classification with neural networks
Neurocomputing
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This paper proposes an extension to conventional regression neural networks (NNs) for replacing the point predictions they produce with prediction intervals that satisfy a required level of confidence. Our approach follows a novel machine learning framework, called Conformal Prediction (CP), for assigning reliable confidence measures to predictions without assuming anything more than that the data are independent and identically distributed (i.i.d.). We evaluate the proposed method on four benchmark datasets and on the problem of predicting Total Electron Content (TEC), which is an important parameter in trans-ionospheric links; for the latter we use a dataset of more than 60000 TEC measurements collected over a period of 11 years. Our experimental results show that the prediction intervals produced by our method are both well calibrated and tight enough to be useful in practice.