Hyperparameter Selection with Good Region Recognition for SVM Based Fault Diagnosis

Siyu Lei, Yue Guo

Abstract


This paper proposes a novel method of good region recognition for hyperparameter selection of SVM. The method can
provide a much smaller good region for optimization search-based methods, and thus it can greatly save computation time. Experimental
results show that the proposed method improves effi ciency of fault diagnosis of rolling bearing with no accuracy loss.

Keywords


good region recognition; hyperparameter selection; support vector machine; fault diagnosis

Full Text:

PDF

Included Database


References


[1]V. Vapnik, The Nature of Statistical Learning Theory. Springer Berlin Heidelberg: Springer Berlin Heidelberg, 2000.

[2]X. H. Liu, P. Cohen, M. Berthold, S. R. Gunn, M. Brown, and K. M. Bossley, “Network performance assessment for Neurofuzzy data modelling,” in

Advances in Intelligent Data Analysis Reasoning about Data, X. H. Liu, P. Cohen and M. Berthold, Eds. Springer Berlin Heidelberg: Springer Berlin

Heidelberg, vol.1280, pp. 313-323, 1997.

[3]D. Lowe and D. Broomhead, “Multivariable functional interpolation and adaptive networks,” Complex Systems, vol. 2, pp. 321-355, 1988.

[4]C. Hsu and C. Lin, “A comparison of methods for multiclass support vector machines,” Neural Networks, vol. 13, no.2, pp. 415-425, 2002.

[5]S. S. Keerthi and C. J. Lin, “As ymptotic behaviors of support vector machines with Gaussian kernel,” Neural Computation, vol. 15, no. 7, pp. 1667-1689,

2003.

[6]Bo okstein and Abraham, “Informetric distributions, part I: Unifi ed overview”, American Society for Information Science, vol. 41, no.5, pp. 368-375, 1990.

[7]M. Varewyck and J. P. Martens, “A practical approach to model selection for support vector machines with a Gaussian kernel,” in Proceeding of IEEE

Transactions on Systems, Man, and Cybernetics, pp. 330-340, 2011.

[8]Z. L. Liu, M. J. Zuo, X. Zhao, and H. B. Xu, “An analytical approach to fast parameter selection of Gaussian RBF kernel for support vector machine,”

Journal of Information Science and Engineering. Accepted on Mar. 11, 2013.




DOI: https://doi.org/10.18686/esta.v10i2.402

Refbacks

  • There are currently no refbacks.


Copyright (c) 2023 Siyu Lei,Yue Guo