An analytical approach to fast parameter selection of gaussian RBF kernel for support vector machine

Journal of Information Science and Engineering

Volumn 31, Issue 2, 2015, Pages 691-710

  Liu, Zhiliang ^a   Zuo, Ming J ^a,b   Zhao, Xiaomin ^b   Xu, Hongbing ^c  

^a School of Mechanical Aerospace and Civil Engineering   (Singapore)

^b UNIVERSITY OF ALBERTA   (Canada)

^c UNIVERSITY OF ELECTRONIC SCIENCE AND TECHNOLOGY OF CHINA   (China)

Author keywords

Class separability; Distance similarity; Gaussian radial basis function; Parameter selection; Support vector machine

Indexed keywords

FUNCTIONS; GAUSSIAN DISTRIBUTION;

ANALYTICAL ALGORITHMS; ANALYTICAL APPROACH; CLASS SEPARABILITY; COMPUTATIONALLY EFFICIENT; DISTANCE SIMILARITY; GAUSSIAN RADIAL BASIS FUNCTIONS; PARAMETER SELECTION; REAL-WORLD DATASETS;

SUPPORT VECTOR MACHINES;

EID: 84928950049     PISSN: 10162364     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

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