Multiple Kernel Multivariate Performance Learning Using Cutting Plane Algorithm

In this paper, we propose a multi-kernel classifier learning algorithm to optimize a given nonlinear and nonsmoonth multivariate classifier performance measure. Moreover, to solve the problem of kernel function selection and kernel parameter tuning, we proposed to construct an optimal kernel by weig...

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Bibliographic Details
Published in2015 IEEE International Conference on Systems, Man, and Cybernetics pp. 1870 - 1875
Main Authors Wang, Jingbin, Wang, Haoxiang, Zhou, Yihua, McDonald, Nancy
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2015
Subjects
cutting plane algorithm
Hilbert space
Iterative methods
Kernel
Loss measurement
multiple kernel
multivariate performance measures
Optimization
Pattern recognition
Support vector machines
Training
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DOI10.1109/SMC.2015.327

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Summary:In this paper, we propose a multi-kernel classifier learning algorithm to optimize a given nonlinear and nonsmoonth multivariate classifier performance measure. Moreover, to solve the problem of kernel function selection and kernel parameter tuning, we proposed to construct an optimal kernel by weighted linear combination of some candidate kernels. The learning of the classifier parameter and the kernel weight are unified in a single objective function considering to minimize the upper boundary of the given multivariate performance measure. The objective function is optimized with regard to classifier parameter and kernel weight alternately in an iterative algorithm by using cutting plane algorithm. The developed algorithm is evaluated on two different pattern classification methods with regard to various multivariate performance measure optimization problems. The experiment results show the proposed algorithm outperforms the competing methods.
DOI:10.1109/SMC.2015.327