id: 06097855
dt: j
an: 06097855
au: Papadopoulos, Harris; Haralambous, Haris
ti: Reliable prediction intervals with regression neural networks.
so: Neural Netw. 24, No. 8, 842-851 (2011).
py: 2011
pu: Elsevier Science (Pergamon), Boston, MA
la: EN
cc: 
ut: conformal prediction; confidence measures; prediction intervals;
    regression; neural networks; total electron content
ci: 
li: doi:10.1016/j.neunet.2011.05.008
ab: Summary: 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.
rv: