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In this paper, we propose a multi-kernel classifier learning algorithm tooptimize a given nonlinear and nonsmoonth multivariate classifier performancemeasure. Moreover, to solve the problem of kernel function selection and kernelparameter tuning, we proposed to construct an optimal kernel by weighted linearcombination of some candidate kernels. The learning of the classifier parameterand the kernel weight are unified in a single objective function considering tominimize the upper boundary of the given multivariate performance measure. Theobjective function is optimized with regard to classifier parameter and kernelweight alternately in an iterative algorithm by using cutting plane algorithm.The developed algorithm is evaluated on two different pattern classificationmethods with regard to various multivariate performance measure optimizationproblems. The experiment results show the proposed algorithm outperforms thecompeting methods.

Multiple kernel multivariate performance learning using cutting plane algorithm