Support Vector Machines (SVMs) versus Multilayer Perception (MLP) in data classification

Egyptian Informatics Journal

Volumn 13, Issue 3, 2012, Pages 177-183

  Zanaty, E A ^a  

^a Sohag University   (Egypt)

Author keywords

Kernel functions; Neural networks; Quadratic Programming (QP); Support vector machine

Indexed keywords

CLASSIFICATION (OF INFORMATION); MULTILAYER NEURAL NETWORKS; MULTILAYERS; NETWORK LAYERS; NEURAL NETWORKS; QUADRATIC PROGRAMMING; RADIAL BASIS FUNCTION NETWORKS; VECTORS;

CLASSIFICATION ACCURACY; COMPARATIVE ANALYSIS; DATA CLASSIFICATION; GAUSSIAN RADIAL BASIS FUNCTIONS; HIGH DIMENSIONS; KERNEL FUNCTION; MULTI-LAYER PERCEPTION; SUPPORT VECTOR MACHINE (SVMS);

SUPPORT VECTOR MACHINES;

EID: 84870517789     PISSN: 11108665     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eij.2012.08.002     Document Type: Article

References (41)

1. R. Duda, P. Hart, and G. Stork Pattern classification 2nd ed. 2001 Wiley Interscience UK
2. Boser B, Guyon I, Vapnik V, A training algorithm for optimal margin classifiers. In: Proceeding COLT '92 proceedings of the fifth annual workshop on computational learning, Pittsburgh, PA, USA; 1992. p. 144-52.
3. S.J. Taylor, and N. Cristianini Kernel methods for pattern analysis 2004 Cambridge University
4. Osuna E, Freund R, Girosi F. Support vector machines, training and applications. Artificial Intelligence Laboratory, MIT Press, Series, Report, AIM-1602, CBCL-144, 1997.
5. G. Boudat, and F. Anour Kernel-based methods and function approximation International Joint Conference on Neural Networks (IJCNN) 2001 IEEE Press Washington 1244 1249
6. D. Meyer, F. Leisch, and K. Hornik The support vector machines under test Neurocomputing 55 2003 169 186
7. T.M. Huang, V. Kecman, and I. Kopriva Kernel based algorithms for mining huge data sets, supervised, semi-supervised and unsupervised learning 2006 Springer-Verlag Berlin, Heidelberg
8. V. Vapnik The nature of statistical learning theory 1995 Springer New York Inc. New York, NY, USA
9. C. Cortes, and V. Vapnik Support vector networks Mach Learn 20 1995 273 297
10. Schölkopf B. Support vector learning, PhD dissertation, Technical University, Berlin, Germany
11. N. Mohammad Muamer, Sulaiman Norrozila, and Khalaf T. Emad A novel local network intrusion detection system based on support vector machine J Comput Sci 7 2011 1560 1564
12. Vapnik V, Golowich S, Smola A. Support vector method for function approximation. Regression estimation and signal processing. In: Advances in neural information processing systems, vol. 9, no. 9, 1997. p. 281-7.
13. R.C. Williamson, A. Smola, and B. Schölkopf Entropy numbers operators and support vector kernels 1999 MA: MIT Press Cambridge
14. Chapelle O, Schölkopf B. Incorporating invariances in non-linear support vector machines. In: Proc. NIPS; 2001. p. 609-16.
15. Lei H, Govindaraju V. Speeding up multi-class SVM evaluation by PCA and feature selection. Center for Unified Biometrics and Sensors (CUBS); 2005.
16. I.W. Tsang, J.T. Kwok, and P.M. Cheung Core vector machines: fast SVMs training on very large data sets J Mach Learn Res 6 2005 271 363
17. J.T. Kwok, and I.W. Tsang The pre-image problem in kernel method IEEE Trans Neural Networks 15 2004 1517 1525
18. G.H. Bakir, J. Weston, and B. Schölkopf Learning to find pre-images Adv Neural Inf Proc Syst 16 2004 449 456
19. Maji S, Berg AC, Malik J. Classification using intersection kernel support vector machines is efficient. In: Conference on computer vision and pattern recognition, CVPR IEEE; 2008. p. 1-8.
20. B. Haasdonk Feature space interpretation of SVMs with indefinite kernels IEEE Trans Pattern Anal Mach Intell 27 4 2005 482 492
21. T. Hastie, C.W. Hsu, and C.J. Lin A comparison of methods for multi-class support vector machines IEEE Trans Neural Networks 13 2005 415 425
22. E.A. Zanaty, A. Afifi, and R.E. Khateeb Improving the accuracy of support vector machines via a new kernel functions Int J Intell Comput Sci 1 2009 55 67
23. E.A. Zanaty, S. Aljahdali, and R.J. Cripps Accurate support vector machines for data classification Int J Rapid Manuf 1 2 2009
24. Mohammadreza Ektefa, Fatimah Sidi, Hamidah Ibrahim, Marzanah Jabar, and Sara Memar A comparative study in classification techniques for unsupervised record linkage model J Comput Sci 7 2011 341 347
25. Balwant A. Sonkamble, and D. Doye A novel linear-polynomial kernel to construct support vector machines for speech recognition J Comput Sci 7 2011 991 996
26. E.A. Zanaty, and A. Afifi Support vector machine (SVMs) with universal kernel Appl Artif. Intell. 25 7 2011 575 589
27. DeCoste D, Mazzoni D. Fast query optimized kernel machine classification via incremental approximate nearest support vectors. In: Proceedings of the 20th international conference on machine learning, Washington, ICML 2003; 2003. p. 115-22.
28. B. Schölkopf, and A.J. Smola Learning with kernels 2002 The MIT Press Cambridge, MA
29. B.S. Schölkopf, C.J. Mika, P. Burges, R.R. Knirsch, and K.G. Muller Input space versus feature space in kernel-based methods IEEE Trans Neural Network 10 5 1999 1000 1017
30. C.J. Burges, and B. Schölkopf Improving the accuracy and speed of support vector machines Neural Inform Proc Syst 9 1997 375 381
31. C.J. Burges A tutorial on support vector machines for pattern recognition Data Min Knowledge Discovery 2 2 1998 121 167
32. B. Schölkopf, C. Burges, and A. Smola Advances in kernel methods support vector learning 1998 MIT Press
33. S.J. Taylor, P.L. Bartlett, R.C. Willianmson, and M. Anthony Structural risk minimization over data-dependent hierarchies IEEE Trans Inf Theory 5 1998 1926 1940
34. J.T. Making Large-scale SVM learning practical Advances in kernel methods-support vector learning 1999 MIT Press Cambridge 42 56
35. Osowski S, Siwek K, Markiewicz T. MLP and SVM networks: a comparative study. In: Proceedings of the 6th nordic signal processing symposium NORSIG, Espoo, Finland; June 2004. p. 9-11.
36. N. Cristianini, and S.J. Taylor An introduction to support vector machines 2000 Cambridge University Press Cambridge, MA
37. http://archive.ics.uci.edu/ml/.
38. Cristianini N, Campbell C, Taylor SJ. Dynamically adapting kernels in support vector machines. In: Proceedings of the 1998 conference on advances in neural information processing systems II; 1999. p. 204-10.
39. O. Chapelle, V. Vapnik, O. Bousquet, and S. Mukherjee Choosing multiple parameters for support vector machines Mach Learn 46 2002 131 159
40. Y. Bengio Gradient-based optimization of hyper-parameters Neural Comput 12 8 2000 1889 1900
41. R. Rifin, and A. Klautau In defense of one vs. all classification J Mach Learn Res 5 2004 101 141