The nature of statistical learning theory
The nature of statistical learning theory
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
MARK: a boosting algorithm for heterogeneous kernel models
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
A fast iterative algorithm for fisher discriminant using heterogeneous kernels
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Learning the Kernel with Hyperkernels
The Journal of Machine Learning Research
Learning the Kernel Function via Regularization
The Journal of Machine Learning Research
Kernel combination versus classifier combination
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Combining multiple kernels by augmenting the kernel matrix
MCS'10 Proceedings of the 9th international conference on Multiple Classifier Systems
Support-vector-based fuzzy neural network for pattern classification
IEEE Transactions on Fuzzy Systems
Efficient hyperkernel learning using second-order cone programming
IEEE Transactions on Neural Networks
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Kernel combination is meant to improve the performance of single kernels and avoid the difficulty of kernel selection. The most common way of combining kernels is to compute their weighted sum. Usually, the kernels are assumed to exist in independent empirical feature spaces and therefore were combined without considering their relationships. To take these relationships into consideration in kernel combination, we propose the generalized augmentation kernel which is extended by all the single kernels considering their correlations. The generalized augmentation kernel, unlike the weighted sum kernel, does not need to find out the weight of each kernel, and also would not suffer from information loss due to the average of kernels. In the experiments, we observe that the generalized augmentation kernel usually can achieve better performances than other combination methods that do not consider relationship between kernels.