Super-parameter selection for Gaussian-Kernel SVM based on outlier-resisting

Measurement: Journal of the International Measurement Confederation

Volumn 58, Issue , 2014, Pages 147-153

  Wang, Xuesong ^a   Huang, Fei ^a   Cheng, Yuhu ^a  

^a CHINA UNIVERSITY OF MINING AND TECHNOLOGY   (China)

Author keywords

Classification accuracy; Computational complexity; Outlier resisting; Super parameter selection; Support vector machine

Indexed keywords

CLASSIFICATION (OF INFORMATION); COMPUTATIONAL COMPLEXITY; SAMPLING; SCREENING; STATISTICS;

BENCHMARK DATASETS; CLASSIFICATION ACCURACY; GAUSSIAN KERNELS; KERNEL PARAMETER; LEARNING ABILITIES; OUTLIER-RESISTING; PARAMETER SELECTION; PARAMETER SPACES;

SUPPORT VECTOR MACHINES;

EID: 84907858258     PISSN: 02632241     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.measurement.2014.08.019     Document Type: Article

References (12)

1. V.N. Vapnik An overview of statistical learning theory IEEE Trans. Neural Networks 10 5 1999 988 999
2. D.J. Bordoloi, and R. Tiwari Support vector machine based optimization of multi-fault classification of gears with evolutionary algorithms from time-frequency vibration data Measurement: J. Int. Measurement Confederation 55 2014 1 14
3. B.B. Ekici A least squares support vector machine model for prediction of the next day solar insolation for effective use of PV systems Measurement: J. Int. Measurement Confederation 50 1 2014 255 262
4. G. Wei, G. Li, Y. Wu, and X.W. long Application of least squares-support vector machine in system-level temperature compensation of ring laser gyroscope Measurement 44 10 2011 1898 1903
5. T. Joachims Making large scale SVM learning practical B. Schölkopf, C.J.C. Burges, A.J. Smola, Advances in Kernel Methods: Support Vector Learning 1998 MIT Press Cambridge MA
6. J. Huang, X.G. Hu, and F. Yang Support vector machine with genetic algorithm for machinery fault diagnosis of high voltage circuit breaker Measurement 44 6 2011 1018 1027
7. K.H. Zhu, and X.G. Song A roller bearing fault diagnosis method based on hierarchical entropy and support vector machine with particle swarm optimization algorithm Measurement: J. Int. Measurement Confederation 47 1 2014 669 675
8. S.S. Keerthi, and C.J. Lin Asymptotic behaviors of support vector machines with Gaussian kernel Neural Comput. 15 7 2003 1667 1689
9. M. Varewyck, and J.P. Martens A practical approach to model selection for support vector machines with a Gaussian kernel IEEE Trans. Syst. Man Cybernet. - Part B: Cybernet. 41 2 2011 330 340
10. J.D. Rodriguez, A. Perez, and J.A. Lozano Sensitivity analysis of k-fold cross validation in prediction error estimation IEEE Trans. Pattern Anal. Machine Intel. 32 3 2010 569 575
11. K. Duan, S.S. Keerthi, and A.N. Poo Evaluation of simple performance measures for tuning SVM hyperparameters Neurocomputing 51 1 2003 41 59
12. C.M. Bishop Pattern recognition and machine learning 2006 Springer New York