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Approximate Confidence and Prediction Intervals for Least Squares Support Vector Regression.
- Source :
- IEEE Transactions on Neural Networks; 01/01/2011, Vol. 22 Issue 1, p110-120, 11p
- Publication Year :
- 2011
-
Abstract
- Bias-corrected approximate 100(1-\alpha)\% 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10459227
- Volume :
- 22
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Neural Networks
- Publication Type :
- Academic Journal
- Accession number :
- 57254346
- Full Text :
- https://doi.org/10.1109/TNN.2010.2087769