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Computational Intelligence and Neuroscience
Volume 2018, Article ID 1018789, 7 pages
Research Article

A Multiple Kernel Learning Model Based on -Norm

1School of Information, Renmin University of China, Beijing 100872, China
2School of Computer Science and Technology, Huaiyin Normal University, Huai’an, Jiangsu 223300, China

Correspondence should be addressed to Xun Liang; moc.361@gnail__nux

Received 29 July 2017; Revised 7 December 2017; Accepted 24 December 2017; Published 23 January 2018

Academic Editor: Toshihisa Tanaka

Copyright © 2018 Jinshan Qi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


By utilizing kernel functions, support vector machines (SVMs) successfully solve the linearly inseparable problems. Subsequently, its applicable areas have been greatly extended. Using multiple kernels (MKs) to improve the SVM classification accuracy has been a hot topic in the SVM research society for several years. However, most MK learning (MKL) methods employ -norm constraint on the kernel combination weights, which forms a sparse yet nonsmooth solution for the kernel weights. Alternatively, the -norm constraint on the kernel weights keeps all information in the base kernels. Nonetheless, the solution of -norm constraint MKL is nonsparse and sensitive to the noise. Recently, some scholars presented an efficient sparse generalized MKL (- and -norms based GMKL) method, in which    established an elastic constraint on the kernel weights. In this paper, we further extend the GMKL to a more generalized MKL method based on the -norm, by joining - and -norms. Consequently, the - and -norms based GMKL is a special case in our method when . Experiments demonstrated that our - and -norms based MKL offers a higher accuracy than the - and -norms based GMKL in the classification, while keeping the properties of the - and -norms based on GMKL.