Title :
An efficient method for computing leave-one-out error in support vector machines with Gaussian kernels
Author :
Lee, Martin M S ; Keerthi, S. Sathiya ; Ong, Chong Jin ; DeCoste, Dennis
Author_Institution :
Dept. of Mech. Eng., Nat. Univ. of Singapore, Singapore
fDate :
5/1/2004 12:00:00 AM
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.
Keywords :
Gaussian processes; iterative methods; support vector machines; Gaussian kernels; benchmark datasets; hyperparameter tuning; iterative decomposition methods; leave-one-out error computing; model comparison; support vector machines; Iterative methods; Kernel; Laboratories; Lagrangian functions; Learning systems; Machine learning; Mechanical engineering; Propulsion; Support vector machine classification; Support vector machines; Computing Methodologies; Normal Distribution; Research Design;
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2004.824266
