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article · 2010

Multiple Kernel Learning with High Order Kernels

Shuhui Wang, Shuqiang Jiang, Qingming Huang, Qi Tian

Cited by 9Abstract and details only

Abstract

Previous Multiple Kernel Learning approaches (MKL) employ different kernels by their linear combination. Though some improvements have been achieved over methods using single kernel, the advantages of employing multiple kernels for machine learning are far from being fully developed. In this paper, we propose to use “high order kernels” to enhance the learning of MKL when a set of original kernels are given. High order kernels are generated by the products of real power of the original kernels. We incorporate the original kernels and high order kernels into a unified localized kernel logistic regression model. To avoid over-fitting, we apply group LASSO regularization to the kernel coefficients of each training sample. Experiments on image classification prove that our approach outperforms many of the existing MKL approaches.

Cite this paper

APA 7

Wang, S., Jiang, S., Huang, Q., & Tian, Q. (2010). Multiple Kernel Learning with High Order Kernels. https://doi.org/10.1109/icpr.2010.524

MLA 9

Wang, Shuhui, et al. “Multiple Kernel Learning with High Order Kernels.” vol. 5, 2010, pp. 2138–41, https://doi.org/10.1109/icpr.2010.524.

Chicago

Wang, Shuhui, Shuqiang Jiang, Qingming Huang, and Qi Tian. 2010. “Multiple Kernel Learning with High Order Kernels.” https://doi.org/10.1109/icpr.2010.524.

Harvard

Wang, S. et al. (2010) ‘Multiple Kernel Learning with High Order Kernels’. Available at: https://doi.org/10.1109/icpr.2010.524.

BibTeX
@article{wang2010multiple,
  title = {{Multiple Kernel Learning with High Order Kernels}},
  author = {Wang, Shuhui and Jiang, Shuqiang and Huang, Qingming and Tian, Qi},
  volume = {5},
  pages = {2138--2141},
  year = {2010},
  doi = {10.1109/icpr.2010.524}
}

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