The projective SUMT method for convex programming
Mathematics of Operations Research
The nature of statistical learning theory
The nature of statistical learning theory
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Dynamically adapting kernels in support vector machines
Proceedings of the 1998 conference on Advances in neural information processing systems II
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
Radius margin bounds for support vector machines with the RBF kernel
Neural Computation
Learning the Kernel Function via Regularization
The Journal of Machine Learning Research
Evolutionary tuning of multiple SVM parameters
Neurocomputing
Automatic model selection for the optimization of SVM kernels
Pattern Recognition
Efficient tuning of SVM hyperparameters using radius/margin bound and iterative algorithms
IEEE Transactions on Neural Networks
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Automatic tuning of hyperparameter and parameter is an essential ingredient and important process for learning and applying Support Vector Machines (SVM). Previous tuning methods choose hyperparameter and parameter separately in different iteration processes, and usually search exhaustively in parameter spaces. In this paper we propose and implement a new tuning algorithm that chooses hyperparameter and parameter for SVM simultaneously and search the parameter space efficiently with a deliberate initialization of a pair of starting points. First we derive an approximate but effective radius margin bound for soft margin SVM. Then we combine multiparameters of SVM into one vector, converting the two separate tuning processes into one optimization problem. Further we discuss the implementation issue about the new tuning algorithm, and that of choosing initial points for iteration. Finally we compare the new tuning algorithm with old gradient based method and cross validation on five benchmark data sets. The experimental results demonstrate that the new tuning algorithm is effective, and usually outperforms those classical tuning algorithms.