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Approximate Confidence and Prediction Intervals for Least Squares Support Vector Regression

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4 Author(s)
De Brabanter, K. ; Dept. of Electr. Eng., Katholieke Univ. Leuven, Leuven, Belgium ; De Brabanter, J. ; Suykens, J.A.K. ; De Moor, B.

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.

Published in:

Neural Networks, IEEE Transactions on  (Volume:22 ,  Issue: 1 )

Date of Publication:

Jan. 2011

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