Parameter optimization of support vector machine for classification using niche genetic algorithm

Nanjing Li Gong Daxue Xuebao/Journal of Nanjing University of Science and Technology

Volumn 33, Issue 1, 2009, Pages 16-20

  Zhu, Ning ^a   Feng, Zhi Gang ^a   Wang, Qi ^a  

^a HARBIN INSTITUTE OF TECHNOLOGY   (China)

Author keywords

Classification; Niche genetic algorithm; Parameter optimization; Sharing functions; Support vector machine

Indexed keywords

ECOLOGY; FUNCTION EVALUATION; GENETIC ALGORITHMS; HAMMING DISTANCE; IMAGE RETRIEVAL; STRUCTURAL OPTIMIZATION; VECTORS;

CLASSIFICATION; NICHE GENETIC ALGORITHM; PARAMETER OPTIMIZATION; SHARING FUNCTIONS; SUPPORT VECTOR MACHINE;

SUPPORT VECTOR MACHINES;

EID: 62549105746     PISSN: 10059830     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (15)

1. Vapnik V. The nature of statistics learning theory [M]. New York: Springer Verlag, 1995.
2. Vapnik V. Statistical learning theory [M]. New York: John Wiley, 1998.
3. Wang Wenjian, Xu Zongben, Lu Weizhen, et al. Determination of the spread parameter in the Gaussian kernel for classification and regression [J]. Neurocomputing, 2003, 55: 653-663.
4. Staelin C. Parameter selection for support vector machines [DB/OL]. http://www.hpl.hp.com/techreports/2002/HPL-2002-354R1.html. 2008-01-06.
5. Zhao Q, Principe J C. Support vector machines for SAR automatic target recognition [J]. IEEE Transactions on Aerospace and Electronic Systems, 2001, 37(2): 643-654.
6. Broomhead D S, Lowe D. Multivariable functional interpolation and adaptive networks [J]. Complex Systems, 1988, 2: 321-355.
7. Ratsch G. Benchmark data sets [DB/DL]. http://ida.first.fraunhofer.de/projects/bench/.
8. Raetsch G, Onoda T, Mueller K-R. Soft margins for adaboost [J]. Machine Learning, 2001, 42(3): 287-320.