A comparative analysis of support vector machines and extreme learning machines

Neural Networks

Volumn 33, Issue , 2012, Pages 58-66

  Liu, Xueyi ^a,b   Gao, Chuanhou ^c   Li, Ping ^a,c  

^a ZHEJIANG UNIVERSITY   (China)

^b CHINA JILIANG UNIVERSITY   (China)

^c ZHEJIANG UNIVERSITY   (China)

Author keywords

Computational complexity; ELM; Generalization ability; SVM; VC dimension

Indexed keywords

COMPARATIVE ANALYSIS; COMPUTATIONAL COSTS; COMPUTATIONAL SPEED; ELM; EXTREME LEARNING MACHINE; GENERALIZATION ABILITY; HIDDEN NODES; LEARNING APPROACH; MODEL SELECTION; RESEARCH EFFORTS; SMALL SAMPLES; SVM; TRAINING SAMPLE; VAPNIK-CHERVONENKIS DIMENSIONS; VC DIMENSION;

COMPUTATIONAL COMPLEXITY; LEARNING ALGORITHMS; SAMPLING;

SUPPORT VECTOR MACHINES;

ARTICLE; CONTROLLED STUDY; EXTREME LEARNING MACHINE; MACHINE LEARNING; PRIORITY JOURNAL; PROBABILITY; SAMPLE SIZE; SUPPORT VECTOR MACHINE; THEORETICAL STUDY;

ARTIFICIAL INTELLIGENCE; DATABASES, FACTUAL; HUMANS; SUPPORT VECTOR MACHINES;

EID: 84863876822     PISSN: 08936080     EISSN: 18792782     Source Type: Journal    
DOI: 10.1016/j.neunet.2012.04.002     Document Type: Article

References (34)

1. Aizerman M., Braverman E., Rozonoer L. Theoretical foundations of the potential function method in pattern recognition learning. Automation and Remote Control 1964, 25:821-837.
2. Akshay U., Shamel S., Sunil S. Prediction of melting points of organic compounds using extreme learning machines. Industrial and Engineering Chemistry Research 2008, 47(3):920-925.
3. Blake C.L., Merz C.J. UCI repository of machine learning databases 1998, University of California, Irvine.
4. Burges C. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 1998, 2(2):121-167.
5. Cortes C., Vapnik V. Support-vector networks. Machine Learning 1995, 20(3):273-297.
6. Cheng G.-J., Cai L., Pan H.-X. Comparison of extreme learning machine with support vector regression for reservoir permeability prediction. Proceeding of international conference on computational intelligence and security, vol. 2 2009, 173-176. IEEE CS.
7. Cherkassky V., Shao X.H., Mulier F.M., Vapnik V.N. Model complexity control for regression using VC generalization bounds. IEEE Transactions on Neural Networks 1999, 10(5):1075-1088.
8. Deng, W.Y., Zheng, Q.H., & Chen, L. 2009. Regularized extreme learning machine, In Proceedings of IEEE symposium on computational intelligence and data mining, pp. 389-395.
9. Feng G., Huang G.B., Lin Q., Gay R. Error minimized extreme learning machine with growth of hidden nodes and incremental learning. IEEE Transactions on Neural Networks 2009, 20(3):1352-1357.
10. Golub G., Charles F., Loan V. Matrix computations 1983, Johns Hopkins University Press, Baltimore.
11. Hsu C.W., Lin C.J. A comparison of methods for multiclass support vector machines. IEEE Transactions on Neural Networks 2002, 13(2):415-425.
12. Huang G.-B. Learning capability and storage capacity of two-hiddenlayer feedforward networks. IEEE Transactions on Neural Networks 2003, 14(2):274-281.
13. Huang G.B., Chen L. Convex incremental extreme learning machine. Neurocomputing 2007, 70:3056-3062.
14. Huang G.B., Chen L. Enhanced random search based incremental extreme learning machine. Neurocomputing 2008, 71:3460-3468.
15. Huang G.B., Chen L., Siew C.K. Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Transactions on Neural Networks 2006, 17(4):879-892.
16. Huang G.B., Ding X., Zhou H. Optimization method based extreme learning machine for classification. Neurocomputing 2010, 74:155-163.
17. Huang G.B., Wang D.H., Lan Y. Extreme learning machine: a survey. International Journal of Machine Learning and Cybernetics 2011, 2:107-122.
18. Huang G.B., Zhou H.M., Ding X.J., Zhang R. Extreme learning machine for regression and multiclass classification. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 2011, 42(2):512-529.
19. Huang G.B., Zhu Q.Y., Siew C.K. Extreme learning machine: a new learning scheme of feedforward neural networks. Proceeding of international joint conference on neural networks. vol. 2 2004, 985-990. IEEE Inc.
20. Huang G.B., Zhu Q.-Y., Siew C.-K. Extreme learning machine: theory and applications. Neurocomputing 2006, 70:489-501.
21. Jonsson K., Kittler J., Li Y.P., Matas J. Support vector machines for face authentication. Journal of Image and Vision Computing 2002, 20:369-375.
22. Liang N.Y., Huang G.B., Saratchandran P., Sundararajan N. A fast and accurate online sequential learning algorithm for feedforward networks. IEEE Transactions on Neural Networks 2006, 17(6):1411-1423.
23. Liu Y., Loh H.-T., Tor S.-B. Comparison of extreme learning machine with support vector machine for text classification. Proceeding of 18th international conference on innovations in applied artificial intelligence: vol. 3533 2005, 390-399. Springer Verlag.
24. Lu, J.W., Plataniotis, K.N., & Ventesanopoulos, A.N. 2001. Face recognition using feature optimization and v-support vector learning, In Proceedings of IEEE Signal Processing Society Workshop, Vol. 6, pp. 373-382.
25. Platt J.C. Fast training of support vector machines using sequential minimal optimization. Advances in kernel methods-support vector learning 1999, 185-208. MIT Press, Cambridge, MA. B. Schölkopf, C. Burges, A. Smola (Eds.).
26. Poggio, T., Mukherjee, S., Rifkin, R., Rakhlin, A., & Verri, A. 2001. b, A. I. Memo No. 2001-011, CBCL Memo 198, Artificial Intelligence Laboratory, Massachusetts Institute of Technology.
27. Rong H.J., Huang G.B., Saratchandran P., Sundararajan N. On-line sequential fuzzy extreme learning machine for function approximation and classification problems. IEEE Transactions on System, Man and Cybernetics, Part B: Cybernetics 2009, 39(4):1067-1072.
28. Serre D. Matrices: theory and applications 2002, Springer-Verlag, New York.
29. Sontag E.D. VC-dimension of neural networks. Neural Networks and Machine Learning 1998, 69-95. Springer, Berlin. C.M. Bishop (Ed.).
30. Vapnik V. The nature of statistical learning theory 1995, Springer Verlag, New York.
31. Vapnik V. Statistical learning theory 1998, Wiley, New York.
32. Wang Y.G., Cao F.L., Yuan Y.B. A study on effectiveness of extreme learning machine. Neurocomputing 2011, 74:2483-2490.
33. Wei X.K., Li Y.H., Feng Y. Comparative study of extreme learning machine and support vector machine. Proceeding of 3rd international symposium on neural networks, vol. 3971 2006, 1089-1095. Springer Verlag.
34. Zhu Q.Y., Qin A.K., Suganthan P.N., Huang G.-B. Evolutionary extreme learning machine. Pattern Recognition 2005, 38:1759-1763.