Journal
NEUROCOMPUTING
Volume 74, Issue 18, Pages 3816-3822Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.neucom.2011.07.017
Keywords
Gaussian kernel function; Noncentral chi-square distribution; Supervised learning; Unsupervised learning
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Funding
- National Natural Science Foundation of China [60704047, 61175024]
- Foundation for the Author of National Excellent Doctoral Dissertation of PR China [201048]
- Shanghai Pujiang Program
- Shanghai Municipal Education Commission [10ZZ17]
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The Gaussian kernel function implicitly defines the feature space of an algorithm and plays an essential role in the application of kernel methods. The parameter of Gaussian kernel function is a scalar that has significant influences on final results. However, until now, it is still unclear how to choose an optimal kernel parameter. In this paper, we propose a novel data-driven method to optimize the Gaussian kernel parameter, which only depends on the original dataset distribution and yields a simple solution to this complex problem. The proposed method is task irrelevant and can be used in any Gaussian kernel-based approach, including supervised and unsupervised machine learning. Simulation experiments demonstrate the efficacy of the obtained results. A user-friendly online calculator is implemented at: www.csbio.sjtu.edu.cn/bioinf/kernel/ for public use. (C) 2011 Elsevier B.V. All rights reserved.
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