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| Data-Dependent Kernel Function Based Kernel Optimization Algorithm |
| LI Jun-Bao1,2, GAO Hui-Jun2 |
1.Department of Automatic Test and Control,Harbin Institute of Technology,Harbin 150001
2.Department of Control Science and Engineering,Harbin Institute of Technology,Harbin 150001 |
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Abstract To solve the selection problem of kernel function and its parameters in kernel learning to enhance the performance of the algorihtm, data-dependent kernel function based kernel optimization method is proposed in this paper. The optimal objective function is built through the maximum margin criterion to solve the optimal parameter of data-dependent kernel. Experimental results show that the proposed algorithm can effectively increase the performance of kernel learning machine.
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Received: 18 June 2009
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[1] Wang Lei, Chan K L, Xue Ping. A Criterion for Optimizing Kernel Parameters in KBDA for Image Retrieval. IEEE Trans on Systems, Man and Cybernetics, 2005, 35(3): 556-562
[2] Huang Jian, Yuen P C, Chen Wensheng, et al. Kernel Subspace LDA with Optimized Kernel Parameters on Face Recognition // Proc of the 6th IEEE International Conference on Automatic Face and Gesture Recognition. Southampton, UK, 2004: 1352-1355
[3] Chen Wensheng, Yuen P C, Huang Jian, et al. Kernel Machine-Based One-Parameter Regularized Fisher Discriminant Method for Face Recognition. IEEE Trans on Systems, Man and Cybernetics, 2005, 35(4): 659-669
[4] Micchelli C A, Pontil M. Learning the Kernel Function via Regularization. Journal of Machine Learning Research. 2005, 6: 1099-1125
[5] Lanckriet G R G, Cristianini N, Bartlett P, et al. Learning the Kernel Matrix with Semidefinte Programming. Journal of Machine Learning Research. 2004, 5: 27-72
[6] Dai Guang, Yeung D Y. Kernel Selection for Semi-Supervised Kernel Machines // Proc of the 24th International Conference on Machine Learning. Corvalis, Oregon, 2007: 1457-1465
[7] Xiong Huilin, Swamy M N S, Ahmad M O. Optimizing the Kernel in the Empirical Feature Space. IEEE Trans on Neural Networks. 2005, 16(2): 460-474
[8] Cristianini N, Kandola J, Elisseeff A, et al. On Kernel Target Alignment // Dietterich T G, Becker S, Ghahramani Z. Advances in Neural Information Processing Systems. Cambridge, USA: MIT Press, 2002, XIV: 367-373
[9] Amari S, Wu S. Improving Support Vector Machine Classifiers by Modifying Kernel Functions. Neural Networks, 1999, 12(6): 783-789
[10] Tao Qing, Chu Dejun, Wang Jue. Recursive Support Vector Machines for Dimensionality Reduction. IEEE Trans on Neural Networks, 2008, 19(1): 189-193
[11] Li Haifeng, Jiang Tao, Zhang Keshu. Efficient and Robust Feature Extraction by Maximum Margin Criterion. IEEE Trans on Neural Networks, 2006, 17(1): 157-165
[12] Samaria F S, Harter A C. Parameterisation of a Stochastic Model for Human Face Identification // Proc of the 2nd IEEE Workshop on Applications of Computer Vision. Sarasota, USA, 1994: 138-142 |
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2010, Vol. 23 
