An efficient method for computing leave-one-out error in support vector machines with Gaussian kernels.
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
Lee
Martin M S
MM
Department of Mechanical Engineering, National University of Singapore, Republic of Singapore.
Keerthi
S Sathiya
SS
Ong
Chong Jin
CJ
DeCoste
Dennis
D
D003205:Computing Methodologies; D016011:Normal Distribution; D012107:Research Design / Q000706:statistics & numerical data