Abstract
The traditional multiple kernel learning (MKL) is usually based on implicit kernel mapping and adopts a certain combination of kernels instead of a single kernel. MKL has been demonstrated to have a significant advantage to the single-kernel learning. Although MKL sets different weights to different kernels, the weights are not changed over the whole input space. This weight setting might not been fit for those data with some underlying local distributions. In order to solve this problem, Gönen and Alpaydın (2008) introduced a localizing gating model into the traditional MKL framework so as to assign different weights to a kernel in different regions of the input space. In this paper, we also integrate the localizing gating model into our previous work named MultiK-MHKS that is an effective multiple empirical kernel learning. In doing so, we can get multiple localized empirical kernel learning named MLEKL. Our contribution is that we first establish a localized formulation in the empirical kernel learning framework. The experimental results on benchmark data sets validate the effectiveness of the proposed MLEKL.
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Acknowledgments
The authors thank Natural Science Foundations of China under Grant No. 60903091, 61272198, and 21176077, the Specialized Research Fund for the Doctoral Program of Higher Education under Grant No. 20090074120003 for support.
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Wang, Z., Xu, J., Gao, D. et al. Multiple empirical kernel learning based on local information. Neural Comput & Applic 23, 2113–2120 (2013). https://doi.org/10.1007/s00521-012-1161-5
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DOI: https://doi.org/10.1007/s00521-012-1161-5