SIAM Review
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
Asymptotic behaviors of support vector machines with Gaussian kernel
Neural Computation
A generalized kernel approach to dissimilarity-based classification
The Journal of Machine Learning Research
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Input space versus feature space in kernel-based methods
IEEE Transactions on Neural Networks
Face verification with a kernel fusion method
Pattern Recognition Letters
Combination of kernels applied to face verification
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
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In this paper we propose alternative methods to parameter selection techniques in order to build a kernel matrix for classification purposes using Support Vector Machines (SVMs). We describe several methods to build a unique kernel matrix from a collection of kernels built using a wide range of values for the unkown parameters. The proposed techniques have been successfully evaluated on a variety of artificial and real data sets. The new methods outperform the best individual kernel under consideration and they can be used as an alternative to the parameter selection problem in kernel methods.