Support vector machines (SVMs) with universal kernels

Applied Artificial Intelligence

Volumn 25, Issue 7, 2011, Pages 575-589

  Zanaty, E A ^a   Afifi, Ashraf ^a  

^a TAIF UNIVERSITY   (Saudi Arabia)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); RADIAL BASIS FUNCTION NETWORKS;

CLASSIFICATION ACCURACY; DATA SET; GAUSSIANS; KERNEL FUNCTION; MERCER CONDITION; NONLINEAR DATA SET; PROPERTY; RADIAL BASIS; RADIAL FUNCTIONS; SUPPORT VECTORS MACHINE;

SUPPORT VECTOR MACHINES;

EID: 79961234710     PISSN: 08839514     EISSN: 10876545     Source Type: Journal    
DOI: 10.1080/08839514.2011.595280     Document Type: Article

References (33)

1. Aronszajn, N. 1950. Theory of reproducing kernels. Transactions of the American Mathematical Society 68:337-404.
2. Bakir, G. H., J. Weston, and B. Schölkopf. 2004. Learning to find pre-images. Advances in Neural Information Processing Systems 16:449-456.
3. Boser, B. E., I. M. Guyon, and V. N. Vapnik. 1995. A training algorithm for optimal margin classifiers, In Proceedings of the fifth annual workshop on computional learning theory, 144-152.
4. Burges, C. J. C., and B. Schölkopf. 1996. Improving the accuracy and speed of support vector machines. In Advances in neural information processing systems. 375-381. Cambridge, MA: MIT Press.
5. Burges, C. J. C. 1998. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2:121-167. (Pubitemid 128695475)
6. Chapelle, O., and B. Schölkopf. 2002. Incorporating invariances in nonlinear support vector machines. In Advances in Neural Information Processing Systems, eds. T. G. Dietterich, S. Becker, and Z. Ghahramani, 14: 594-609, Cambridge, MA: MIT Press.
7. Cortes, C., and V. N. Vapnik. 1995. Support - vector networks. Machine Learning 20:273-297.
8. Cuturi, M. 2007. Permanents, transport polytopes and positive definite kernels on histograms. In Proceedings of the 20th international joint conference on artificial intelligence, 732-737.
9. Haasdonk, B. 2002. Feature space interpretation of SVMs with indefinite kernels. IEEE Transactions on Pattern Analysis and Machine Intelligence 27, no. 4.
10. Hansheng, L., and V. Govindaraju. 2005. Speeding up multiclass SVM evaluation by PCA and feature selection. Center for Unified Biometrics and Sensors (CUBS).
11. Hastie, T., C. W. Hsu, and C. J. Lin. 2005. A comparison of methods for multiclass support vector machines. IEEE Transactions on Neural Networks 13:415-425.
12. Kwok, J. T., and I. W. Tsang. 2004. The pre-image problem in kernel methods. IEEE Transactions on Neural Networks 15:1517-1525.
13. Mangasarian, O.L. and D. R. Musicant. 1999. Successive overrelaxation for support vector machines. IEEE Transactions on Neural Networks 10 (5): 1032-1037.
14. Maji, S., A. C. Berg, J. Malik. 2008. Classification using intersection kernel support vector machines is efficient. IEEE Conference on Computer Vision and Pattern Recognition, June 1-8.
15. Mercer, T. 1909. Functions of positive and negative type and their connection with the theory of integral equations. Philosophical Transactions of the Royal Society of London Series A:415-446.
16. Platt, J. 1999. Advances in kernel methods: Support vector learning. Chapter, Fast training of support vector machines using sequential minimal optimization, 255-268. Cambridge, MA: The MIT Press.
17. Rifin, R., and A. Klautau. 2004. In defense of one vs. all classification. Journal of Machine Learning Research 5:101-141.
18. Schölkopf, B., and A. J. Smola. 2002. Learning with kernels. Cambridge, MA: The MIT Press.
19. Schölkopf, B. 1997. Support vector learning. PhD dissertation, Technische Universitat Berlin, Germany.
20. Shawe-Taylor, J., P. L. Bartlett, R. C. Williamson, and M. Anthony. 1998. Structural risk minimization over data-dependent hierarchies. IEEE Transactions on Information Theory 44 (5): 1926-1940. (Pubitemid 128738561)
21. Sonnenberg, S., G. Rätsch, C. Schäfer, and B. Schölkopf. 2006. Large scale multiple kernel learning. Journal of Machine Learning Research 7:1531-1565. (Pubitemid 44373694)
22. Tan, Y., and J. Wang. 2004. A Support vector machine with a hybrid kernel and minimal Vapnik- Chervonenkis dimension. IEEE Transactions on Knowledge and Data Engineering 16 (4), April.
23. Tsang, I. W., J. T. Kwok, P-M. Cheung. 2005. Core vector machines: Fast SVMs training on very large data sets. Journal of Machine Learning Research 6:271-363.
24. Vapnik, V. N. 1995. The nature of statistical learning theory. New York: Springer Verlag.
25. Vapnik, V. N. 1999. An overview of statistical learning theory. IEEE Transactions on Neural Networks 10 (5): 988-999.
26. Vapnik, V., S. Golowich, and A. Smola. 1997. Support vector method for function approximation, regression estimation and signal processing. Advances in Neural Information Processing Systems 9. Cambridge, Mass.: MIT Press.
27. Web1: UCI Machine Learning Repository, available online at http://www.ics.uci.edu/~mlearn/MLRepository. html.
28. Web2: Artificial Intelligence and Computer Science Laboratory, available online at http://www.liacc. up.pt/ML/old/statlog/datasets.html1.
29. Williamson, R. C., A. Smola, and B. Schölkopf. 1999. Entropy numbers, operators and support vector kernels. 44-127, Cambridge, MA: MIT Press.
30. Williamson, R. C., A. J. Smola, and B. Schölkopf. 1998. Generalization performance of regularization networks and support vector machines via entropy numbers of compact operators. Technical Report 19, NeuroCOLT.
31. Zanaty, E. A., and S. Aljahdali. 2008. Improving the accuracy of support vector machines. In Proceedings of 23rd International Conference on Computers and Their Application, 75-83. Cancun, Mexico. April.
32. Zanaty, E. A., A. Afifi, and R. El-Khateeb. 2009. Improving the accuracy of support vector machines via a new kernel function, to appear in the International Journal of Intelligent Computing.
33. Zanaty, E. A, Sultan Aljahdali, and R. J. Cripps. 2009. Accurate support vector machines for data classification. International Journal of Rapid Manufacturing 2009 1 (2): 114-127.