An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Multiple kernel learning, conic duality, and the SMO algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
Large-Scale Kernel Machines (Neural Information Processing)
Large-Scale Kernel Machines (Neural Information Processing)
An efficient kernel matrix evaluation measure
Pattern Recognition
Kernel combination versus classifier combination
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Nonlinear kernel-based statistical pattern analysis
IEEE Transactions on Neural Networks
Optimizing the kernel in the empirical feature space
IEEE Transactions on Neural Networks
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Traditional kernelised classification methods could not perform well sometimes because of the using of a single and fixed kernel, especially on some complicated data sets. In this paper, a novel optimal double-kernel combination (ODKC) method is proposed for complicated classification tasks. Firstly, data sets are mapped by two basic kernels into different feature spaces respectively, and then three kinds of optimal composite kernels are constructed by integrating information of the two feature spaces. Comparative experiments demonstrate the effectiveness of our methods.