The nature of statistical learning theory
The nature of statistical learning theory
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Multiple kernel learning, conic duality, and the SMO algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
More generality in efficient multiple kernel learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Maximum Likelihood Model Selection for 1-Norm Soft Margin SVMs with Multiple Parameters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple Kernel Learning Algorithms
The Journal of Machine Learning Research
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In kernel-based machines, the integration of a number of different kernels to build more flexible learning methods is a promising avenue for research. In multiple kernel learning, a compound kernel is build by learning a kernel that is a positively weighted arithmetic mean of several sources. We show in this paper that the only feasible average for kernel learning is precisely the arithmetic average. We investigate general families of averaging processes and how they relate to the development of kernels. Specifically, a number of multivariate and univariate kernels are developed based on the notion of generalized means. These results can be used in more general kernel optimization procedures.