Please use this identifier to cite or link to this item: https://doi.org/10.1109/TNN.2004.824266
Title: An efficient method for computing leave-one-out error in support vector machines with Gaussian kernels
Authors: Lee, M.M.S.
Keerthi, S.S. 
Ong, C.J. 
DeCoste, D.
Keywords: Leave-one-out (LOO) error
Support vector machines (SVMs)
Issue Date: May-2004
Source: Lee, M.M.S., Keerthi, S.S., Ong, C.J., DeCoste, D. (2004-05). An efficient method for computing leave-one-out error in support vector machines with Gaussian kernels. IEEE Transactions on Neural Networks 15 (3) : 750-757. ScholarBank@NUS Repository. https://doi.org/10.1109/TNN.2004.824266
Abstract: In this paper, we give an efficient method for computing the leave-one-out (LOO) error for support vector machines (SVMs) with Gaussian kernels quite accurately. It is particularly suitable for iterative decomposition methods of solving SVMs. The importance of various steps of the method is illustrated in detail by showing the performance on six benchmark datasets. The new method often leads to speedups of 10-50 times compared to standard LOO error computation. It has good promise for use in hyperparameter tuning and model comparison.
Source Title: IEEE Transactions on Neural Networks
URI: http://scholarbank.nus.edu.sg/handle/10635/59442
ISSN: 10459227
DOI: 10.1109/TNN.2004.824266
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