Machine Learning
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
Estimating the Generalization Performance of an SVM Efficiently
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Bounds on Error Expectation for Support Vector Machines
Neural Computation
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
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Automatic parameters selection is an important issue to make support vector machines (SVMs) practically useful. Most existing approaches use Newton method directly to compute the optimal parameters. They treat parameters optimization as an unconstrained optimization problem. In this paper, the limitation of these existing approached is stated and a new methodology to optimize kernel parameters, based on the computation of the gradient of penalty function with respect to the RBF kernel parameters, is proposed. Simulation results reveal the feasibility of this new approach and demonstrate an improvement of generalization ability.