Gaussian kernel optimization: Complex problem and a simple solution

Neurocomputing

Volumn 74, Issue 18, 2011, Pages 3816-3822

  Yin, Jiang Bo ^a   Li, Tao ^b   Shen, Hong Bin ^a  

^a Department of Automation   (China)

^b SHANGHAI JIAO TONG UNIVERSITY   (China)

Author keywords

Gaussian kernel function; Noncentral chi square distribution; Supervised learning; Unsupervised learning

Indexed keywords

COMPLEX PROBLEMS; DATA SETS; DATA-DRIVEN METHODS; FEATURE SPACE; GAUSSIAN KERNEL FUNCTIONS; GAUSSIAN KERNELS; GAUSSIANS; KERNEL BASED APPROACH; KERNEL METHODS; KERNEL PARAMETER; NONCENTRAL CHI-SQUARE DISTRIBUTION; SIMULATION EXPERIMENTS; UNSUPERVISED MACHINE LEARNING;

OPTIMIZATION; SUPERVISED LEARNING; UNSUPERVISED LEARNING;

GAUSSIAN DISTRIBUTION;

ACCESS TO INFORMATION; ARTICLE; COMPUTER SIMULATION; INFORMATION PROCESSING; INTERNET; KERNEL METHOD; MACHINE LEARNING; MATHEMATICAL ANALYSIS; ONLINE SYSTEM; PRIORITY JOURNAL; PROCESS OPTIMIZATION;

EID: 80053332419     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2011.07.017     Document Type: Article

References (15)

1. Hearst M.A., Dumais S., Osman E., Platt J., Scholkopf B. Support vector machines. IEEE Intelligent Systems and their Applications 1998, 13:18-28.
2. Evgeniou T., Micchelli C.A., Pontil M. Learning multiple tasks with kernel methods. Journal of Machine Learning Research 2006, 6:615-637.
3. Zhang T., Zhang H., Chen K., Shen S., Ruan J., Kurgan L. Accurate sequence-based prediction of catalytic residues. Bioinformatics 2008, 24:2329-2338.
4. Chapelle O., Vapnik V., Bousquet O., Mukherjee S. Choosing multiple parameters for support vector machines. Machine Learning 2002, 46:131-159.
5. Ghiasi-Shirazi K., Safabakhsh R., Shamsi M. Learning translation invariant kernels for classification. Journal of Machine Learning Research 2010, 11:1353-1390.
6. Wu M., lkopf B.Sch, Bakir G. A direct method for building sparse kernel learning algorithms. Journal of Machine Learning Research 2006, 7:603-624.
7. Ye J., Ji S., Chen J. Multi-class discriminant kernel learning via convex programming. Journal of Machine Learning Research 2008, 9:719-758.
8. S. Lafon, Diffusion Maps and Geometric Harmonics., Ph.D. thesis, Yale University, USA, May (2004).
9. Yan S.C., Xu D., Zhang B.Y., Zhang H.J., Yang Q., Lin S. Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Transactions on Pattern Analysis and Machine Intelligence 2007, 29:40-51.
10. Muirhead R.J., ebrary I. Aspects of multivariate statistical theory 1982, Wiley Online Library.
11. I.S. Dhillon, Y. Guan, B. Kulis, Kernel k-means: spectral clustering and normalized cuts, in Proceedings of the Tenth ACM SIGKDD (2004), pp. 551-556.
12. G. Tzortzis, A. Likas, The global kernel k-means clustering algorithm, in Proceedings of the International Joint Conference on Neural Networks (IJCNN) (2008), pp. 1977-1984.
13. C. Blake, C.J. Merz, {UCI} Repository of machine learning databases, 1998.
14. A. Argyriou, C.A. Micchelli, M. Pontil, Learning convex combinations of continuously parameterized basic kernels, in Proceedings of the 18th Annual Conference on Learning Theory (2005), pp. 338-352.
15. A. Argyriou, R. Hauser, C.A. Micchelli, M. Pontil, A DC-programming algorithm for kernel selection, in Proceedings of the 23rd International Conference on Machine Learning (2006), pp. 41-48.