Approximate confidence and prediction intervals for least squares support vector regression.
Bias-corrected approximate 100(1-α)% pointwise and simultaneous confidence and prediction intervals for least squares support vector machines are proposed. A simple way of determining the bias without estimating higher order derivatives is formulated. A variance estimator is developed that works well in the homoscedastic and heteroscedastic case. In order to produce simultaneous confidence intervals, a simple Šidák correction and a more involved correction (based on upcrossing theory) are used. The obtained confidence intervals are compared to a state-of-the-art bootstrap-based method. Simulations show that the proposed method obtains similar intervals compared to the bootstrap at a lower computational cost.
Department of Electrical Engineering, Research Division SCD, Katholieke Universiteit Leuven, Leuven 3001, Belgium. kris.debrabanter@esat.kuleuven.be
Kris
K
De Brabanter
Jos
J
De Brabanter
Johan A K
JA
Suykens
Bart
B
De Moor
D000465:Algorithms; D001185:Artificial Intelligence; D015982:Bias; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D012984:Software / Q000592:standards; D012986:Software Validation