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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Jingbin Wang Haoxiang Wang Yihua Zhou McDonald, N. |
| Copyright Year | 2015 |
| Description | Author affiliation: Nat. Time Service Center, Xian, China (Jingbin Wang) || Dept. of Mech., Lehigh Univ., Bethlehem, PA, USA (Yihua Zhou) || Dept. of Comput. Sci., Tulane Univ., New Orleans, LA, USA (McDonald, N.) || Dept. of Electr. & Comput. Eng., Cornell Univ., Ithaca, NY, USA (Haoxiang Wang) |
| Abstract | 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. |
| Starting Page | 1870 |
| Ending Page | 1875 |
| File Size | 246156 |
| Page Count | 6 |
| File Format | |
| e-ISBN | 9781479986972 |
| DOI | 10.1109/SMC.2015.327 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2015-10-09 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Kernel Hilbert space Optimization Training Loss measurement Support vector machines Iterative methods cutting plane algorithm Pattern recognition multiple kernel multivariate performance measures |
| Content Type | Text |
| Resource Type | Article |
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