On optimal degree selection for polynomial kernel with support vector machines: Theoretical and empirical investigations

International Journal of Knowledge-Based and Intelligent Engineering Systems

Volumn 11, Issue 1, 2007, Pages 1-18

  Ali, Shawkat ^a   Smith Miles, Kate ^b  

^a CENTRAL QUEENSLAND UNIVERSITY   (Australia)

^b DEAKIN UNIVERSITY   (Australia)

Author keywords

Polynomial kernel; Rule based method; Support vector machines

Indexed keywords

EID: 85013615152     PISSN: 13272314     EISSN: 18758827     Source Type: Journal    
DOI: 10.3233/KES-2007-11101     Document Type: Article

References (39)

1. S. Ali and K.A. Smith, Automatic parameter selection for polynomial kernel, Proceedings of the IEEE International Conference on Information Reuse and Integration, 2003, 243-249.
2. S. Ali and K.A. Smith, Laplace kernel with automatic smoothing parameter estimation for support vector machine, Submitted in Journal of Computational Management Science, Springer-Verlag Berlin, Heidelberg, 2004.
3. S. Ali and K.A. Smith, Automatic kernel selection for support vector machines, Accepted for International Journal of Neurocomputing, Elsevier Scienc., 2004.
4. S.-I. Amari and S. Wu, Improving Support Vector Machine Classifiers by Modifying Kernel Functions, Neural Networks 12 (1999), 783-789.
5. K.P. Bennett, S. Wu and L. Auslender, On support vector decision trees for database marketing, IEEE International Joint Conference on Neural Networks (IJCNN '99), 1999, 2, 904-909.
6. C. Blake and C.J. Merz, UCI repository of machine learning databases. http://www.ics.uci.edu/-mlearn/MLRepository.html, Irvine, CA: University of California, 2002.
7. B.E. Boser, I. Guyon and V.N. Vapnik, A Training algorithm for optimal margin classifiers, in proceedings of the Fifth Annual Workshop of Computational Learning Theory, Pittsburg, ACM 5, 1992, 144-152.
8. C. Cortes and V. Vapnik, Support vector networks, Machine Learning 20 (1995), 273-297.
9. Y.L. Cun, J.S. Denker and S.A. Solla, Optimal brain damage, in: Advances in Neural Information Processing Systems, D.S. Touretzky, eds, San Mateo, Morgan Kaufmann, San Mateo, CA, 1990, 2, 598-605.
10. R.P.W. Duin, A note on comparing classifier, Pattern Recognition Letters 1 (1996), 529-536.
11. T. Evgeniou, M. Pontil and T. Poggio, Regularization Networks and Support Vector Machines, Advances in Computational Mathematics 13(1) (2000), 1-50.
12. S. Geman, E. Bienenstock and R. Doursat, Neural networks and the bias/variance dilemma, Neural Computation 4(1) (1992), 1-58.
13. S.R. Gunn, Support vector machines for classification and regression, ISIS Technical Report, Image Speech and Intelligent Systems Group, University of Southampton, UK, 1998.
14. I. Guyon, J. Weston, S. Barnhill and V. Vapnik, Gene selection for cancer classification using support vector machines, Machine Learning 46(1/3) (2002), 389-422.
15. K. Hyun-Chul, P. Shaoning, J. Hong-Mo, K. Daijin and B. Sung-Yang, Pattern classification using support vector machine ensemble, Proceedings of IEEE 16th International Conference on Pattern Recognition, 2002, 2, 160-163.
16. T. Joachims, Text categorization with support vector machines: learning with many relevant features, in: Proceedings of ECML-98, 10th European Conference on Machine Learning, C. N’edellec and C. Rouveirol, eds, 1998, pp. 137-142.
17. R.E. Kass and A.E. Raftery, Bayes factors and model uncertainty. Technical Report 254, University of Washington, 1993.
18. A. Krogh and J.A. Hertz, A simple weight decay can improve generalization, in: Advances in Neural Information Processing Systems, J.E. Moody et al., eds, Morgan Kaufmann, San Mateo, CA, 1992, 4, 950-957.
19. S. Lawrence and C.L. Giles, Overfitting and neural networks: conjugate gradient and backpropagation, in proceedings of the IEEE international joint conference on Neural Networks, 2000, 114-119.
20. T.-S. Lim, Knowledge discovery central, Datasets, 2002, http://www.KDCentral.com/.
21. J. Ma, J. Theiler and S. Perkins, Accurate online support vector regression, Neural Computation 15 (2003), 2683-2703.
22. W. Mandenhall and T. Sincich, Statistics for Engineering and the Sciences, 4th ed., Prentice Hall, 1995.
23. T. Minka, Automatic choice of dimensionality for PCA. Technical Report 514, MIT Media laboratory perceptual computing section, 1999.
24. K. Morik, P. Brockhausen and T. Joachims, Combining statistical learning with a knowledge-based approach-a case study in intensive care monitoring, in Proc. 16th International conf. on Machine Learning, 1999, 268-277.
25. T. Onoda, H. Murata, G. Ratsch and K.-R. Muller, Experimental Analysis of Support Vector Machines with Different Kernels Based on Non-Intrusive Monitoring Data, IEEE Proceedings of the 2002 International Joint Conference on Neural Networks, 2002, 3, 2186-2191.
26. Y.-Y. Ou, C.-Y. Chen, S.-C. Hwang and Y.-J. Oyang, Expediting model selection for support vector machines based on data reduction, IEEE International Conference on Systems, Man and Cybernetics, 2003, 1, 786-791.
27. G. Paab, E. Leopold, M. Larson, J. Kindermann and S. Eickeler, SVM classification using sequences of phonemes and syllables, Proceedings of European Conference on Machine Learning (ECML), Helsinki, 2002.
28. E. Parrado-Hernandez, I. Mora-Jimenez, J. Arenas-Garca, A.R. Figueiras-Vidal and A. Navia-Vazquez, Growing support vector classifiers with controlled complexity, Pattern Recognition 36 (2003), 1479-1488.
29. R. Quinlan, C4.5: Programs for Machine Learning, Morgan Kaufman Publishers, San Mateo, CA, 1993.
30. E.M.D. Santos and H.M. Gomes, A Comparative study of polynomial kernel SVM applied to appearance-based object recognition, Proceedings of the First International Workshop on Pattern Recognition with Support Vector Machines, Lecture Notes in Computer Science, Springer-Verlag, London, 2002, 408-418.
31. K.A. Smith, F. Woo, V. Ciesielski and R. Ibrahim, Modelling the relationship between problem characteristics and data mining algorithm performance using neural networks, in: Smart Engineering System Design: Neural Networks, Fuzzy Logic, Evolutionary Programming, Data Mining, and Complex Systems, C. Dagli et al., eds, ASME Press, 2001, 11, pp. 357-362.
32. K.A. Smith, F.Woo, V. Ciesielski and R. Ibrahim, Matching data mining algorithm suitability to data characteristics using a self-organising map, in: Hybrid Information Systems, A. Abraham and M. Koppen, eds, Physica-Verlag, Heidelberg, 2002, pp. 169-180.
33. Statistics toolbox user’s guide, Version 3, The MathWorks, Inc. USA, 2001.
34. M.E. Tipping and C.M. Bishop, Mixtures of probabilistic principal component analysers, Neural Computation 11(2) (1999), 443-482.
35. M.E. Tipping and C.M. Bishop, Probabilistic principal component analysis, Journal of Royal Statistical Society 61(3) (1999), 611-622.
36. V. Vapnik, The Nature of Statistical Learning Theory, Springer-Verlag, New York, 1995.
37. V. Wan and S. Renals, Evaluation of kernel methods for speaker verification and identification, Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, (ICASSP '02), 2002, 1, 669-672.
38. A.S. Weigend, D.E. Rumelhart and B.A. Huberman, Generalization by weight-elimination with application to forecasting, in: Advances in Neural Information Processing Systems, R.P. Lippmann et al., eds, Morgan Kaufmann, San Mateo, CA, 1991, 3, 875-882.
39. J.Weston and C.Watkins, Multi-class support vector machines, in: Proceedings of 7th European Symposium on Artificial Neural Networks (ESANN99), M. Verleysen, eds, Bruges, Belgium, 1999.