An automatic method for selecting the parameter of the normalized kernel function to support vector machines

Journal of Information Science and Engineering

Volumn 28, Issue 1, 2012, Pages 1-15

  Li, Cheng Hsuan ^a   Ho, Hsin Hua ^b   Liu, Yu Lung ^c   Lin, Chin Teng ^a   Kuo, Bor Chen ^d   Taur, Jin Shiuh ^b  

^a NATIONAL CHIAO TUNG UNIVERSITY   (Taiwan)

^b NATIONAL CHUNG HSING UNIVERSITY   (Taiwan)

^c ASIA UNIVERSITY   (Taiwan)

^d NATIONAL TAICHUNG UNIVERSITY OF EDUCATION   (Taiwan)

Author keywords

K fold cross validation; Kernel method; Normalized kernel; Optimal kernel; Soft margin support vector machine; SVM

Indexed keywords

CROSS VALIDATION; KERNEL METHODS; NORMALIZED KERNEL; OPTIMAL KERNEL; SUPPORT VECTOR; SVM;

ARTIFICIAL INTELLIGENCE;

SUPPORT VECTOR MACHINES;

EID: 84862959226     PISSN: 10162364     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (16)

1. S. T. John and C. Nello, Kernel Methods for Pattern Analysis, Cambridge University Press, Cambridge, 2004.
2. F. Melgani and L. Bruzzone, "Classification of hyperspectral remote sensing images with support vector machines," IEEE Transactions on Geoscience and Remote Sensing, Vol. 42, 2004, pp. 1778-1790.
3. G. Camps-Valls and L. Bruzzone, "Kernel-based methods for hyperspectral image classification," IEEE Transactions on Geoscience and Remote Sensing, Vol. 43, 2005, pp. 1351-1362. (Pubitemid 40811944)
4. G. Camps-Valls, L. Gomez-Chova, J. Munoz-Mari, J. L. Rojo-Alvarez, and M. Martinez-Ramon, "Kernel-based framework for multitemporal and multisource remote sensing data classification and change detection," IEEE Transactions on Geoscience and Remote Sensing, Vol. 46, 2008, pp. 1822-1835. (Pubitemid 351711220)
5. G. Camps-Valls and L. Bruzzone, Kernel Methods for Remote Sensing Data Analysis, John Wiley and Sons, Ltd., London, 2009.
6. G. Camps-Valls, L. Gómez-Chova, J. Calpe, E. Soria, J. D. Martín, L. Alonso, and J. Moreno, "Robust support vector method for hyperspectral data classification and knowledge discovery," IEEE Transactions on Geoscience and Remote Sensing, Vol. 42, 2004, pp. 1530-1542.
7. C. C. Chang and C. J. Lin, "LIBSVM: A library for support vector machines," 2001, http://www.csie.ntu.edu.tw/~cjlin/libsvm.
8. H. L. Xiong, M. N. S. Swamy, and M. O. Ahmad, "Optimizing the kernel in the em pirical feature space," IEEE Transactions on Neural Networks, Vol. 16, 2005, pp. 460-474. (Pubitemid 40390828)
9. B. Chen, H. Liu, and Z. Bao, "Optimizing the data-dependent kernel under a unified kernel optimization framework," Pattern Recognition, Vol. 41, 2007, pp. 2107-2119.
10. E. K. P. Chong and S. H. Zak, An Introduction to Optimization, 3rd ed., John Wiley and Sons, Inc., New York, 2008.
11. D. A. Landgrebe, Signal Theory Methods in Multispectral Remote Sensing, Wiley, Hoboken, New Jersey, 2003.
12. J. A. Benediktsson, J. A. Palmason, and J. R. Sveinsson, "Classification of hyperspectral data from urban areas based on extended morphological profiles," IEEE Transactions on Geoscience and Remote Sensing, Vol. 43, 2005, pp. 480-491. (Pubitemid 40320270)
13. B. S. Sebastiano and M. Gabriele, "Extraction of spectral channels from hyperspectral images for classification purposes," IEEE Transactions on Geoscience and Remote Sensing, Vol. 45, 2007, pp. 484-495.
14. G. Baudat and F. Anouar, "Generalized discriminant analysis using a kernel approach," Neural Computation, Vol. 12, 2000, pp. 2385-2404.
15. B. C. Kuo, C. H. Li, and J. M. Yang, "Kernel nonparametric weighted feature extraction for hyperspectral image classification," IEEE Transactions on Geoscience and Remote Sensing, Vol. 47, 2009, pp. 1139-1155.
16. K. P. Wu and S. D. Wang, "Choosing the kernel parameters for support vector machines by theinter-cluster," Pattern Recognition, Vol. 42, 2009, pp. 710-717.