Choosing Multiple Parameters for Support Vector Machines
Machine Learning
MARK: a boosting algorithm for heterogeneous kernel models
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Ho--Kashyap classifier with generalization control
Pattern Recognition Letters
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
Optimal kernel selection in Kernel Fisher discriminant analysis
ICML '06 Proceedings of the 23rd international conference on Machine learning
Efficient kernel feature extraction for massive data sets
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Large Scale Multiple Kernel Learning
The Journal of Machine Learning Research
Input space versus feature space in kernel-based methods
IEEE Transactions on Neural Networks
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In this paper, we have done research on Multiple Kernel Learning in Empirical Kernel Mapping Space. We find the combination of kernels in empirical kernel mapping space means weighted fusion of empirical kernel mapping samples. And then, we develop a kind of multiple kernel regularized Ho-Kashyap classifier to realize multiple kernel classification in empirical kernel mapping space. The experimental results on benchmark datasets demonstrate the feasibility and effectiveness of the proposed method in empirical kernel mapping space.