Choosing Multiple Parameters for Support Vector Machines
Machine Learning
The LCCP for optimizing kernel parameters for SVM
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
Multiple parameter selection for LS-SVM using smooth leave-one-out error
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
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Proceedings of the 10th annual conference on Genetic and evolutionary computation
A model updating strategy for predicting time series with seasonal patterns
Applied Soft Computing
Hybrid virtual sensor based on RBFN or SVR compared for an embedded application
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part II
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Selection of kernel function parameters is one of the key problems in support vector regression(SVR) for forecasting because these free parameters have significant impact on the performances of forecasting accuracy. The commonly used grid search method is intractable and computational expensive. In this paper, a fast grid search method is proposed for tuning multiple parameters for SVR with RBF kernel for time series forecasting. Empirical results confirm the feasibility and validation of the proposed method.