期刊
IEEE TRANSACTIONS ON NEURAL NETWORKS
卷 22, 期 1, 页码 110-120出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNN.2010.2087769
关键词
Bias; confidence interval; heteroscedasticity; homoscedasticity; kernel-based regression; variance
类别
资金
- Research Council KUL: GOA AMBioRICS
- GOA MaNet
- Optimization in Engineering (OPTEC) [CoE EF/05/006]
- IOF-SCORES4CHEM
- Flemish Government: FWO [G.0452.04, G.0499.04, G.0211.05, G.0226.06, G.0321.06, G.0302.07, G.0320.08, G.0558.08, G.0557.08, G.0588.09, G.0377.09]
- IWT
- McKnow-E
- Eureka-Flite+
- SBO LeCoPro
- SBO Climaqs
- POM
- Belgian Federal Science Policy Office [IUAP P6/04]
- EU: ERNSI
- FP7-HD-MPC [INFSO-ICT-223854]
- COST intelliCIS
- EMBOCOM
- AMINAL
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. Sidak 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.
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