Approximate Confidence and Prediction Intervals for Least Squares Support Vector Regression

Authors:
K. De Brabanter;J. De Brabanter;J. A.K. Suykens;B. De Moor
Affiliations:
Dept. of Electr. Eng., Katholieke Univ. Leuven, Leuven, Belgium;-;-;-
Venue:
IEEE Transactions on Neural Networks
Year:
2011

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Abstract

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.