Optimization-based extreme learning machine with multi-kernel learning approach for classification

Proceedings - International Conference on Pattern Recognition

Volumn , Issue , 2014, Pages 3564-3569

  Cao, Le Le ^a   Huang, Wen Bing ^a   Sun, Fu Chun ^a  

^a TSINGHUA UNIVERSITY   (China)

Author keywords

Extreme learning machine (ELM); Multi kernel extreme learning machine (MK ELM); Multi kernel learning (MKL); Optimization based ELM; SimpleMKL

Indexed keywords

FEEDFORWARD NEURAL NETWORKS; GRADIENT METHODS; KNOWLEDGE ACQUISITION; MULTILAYER NEURAL NETWORKS; OPTIMIZATION; PATTERN RECOGNITION; SUPPORT VECTOR MACHINES;

BINARY CLASSIFICATION PROBLEMS; EXTREME LEARNING MACHINE; GENERALIZATION PERFORMANCE; LOW COMPUTATIONAL COMPLEXITY; MULTI-KERNEL LEARNING; SIMPLEMKL; SINGLE-HIDDEN LAYER FEED-FORWARD NEURAL NETWORK; SUPERVISED LEARNING PROBLEMS;

LEARNING SYSTEMS;

EID: 84919934813     PISSN: 10514651     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICPR.2014.613     Document Type: Conference Paper

References (34)

1. G.-B. Huang, Q.-Y. Zhu, C.-K. Siew, "Extreme learning machine: A new learning scheme of feedforward neural networks," Proceedings of International Joint Conference on Neural Networks, 2004, 2: 985-990
2. G.-B. Huang, Q.-Y. Zhu, C.-K. Siew, "Extreme learning machine: theory and applications," Neurocomputing, 2006, 70(1): 489-501
3. G.-B. Huang, L. Chen, C.-K. Siew, "Universal approximation using incremental constructive feedforward networks with random hidden nodes," IEEE Trans. on Neural Networks, 2006, 17(4): 879-892
4. G.-B. Huang, H. Zhou, X. Ding, R. Zhang, "Extreme learning machine for regression and multiclass classification," Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, 2012, 42(2): 513-529
5. V. N. Vapnik, "The Nature of Statistical Learning Theory," Berlin: Springer, 1995
6. V. N. Vapnik, "Statistical Learning Theory," New York: Wiley, 1998
7. B. Schoelkopf, A. Smola, K. R. Muller, "Nonlinear component analysis as a kernel eigenvalue problem," Neural Comp., 1998, 10(5): 1299-1319
8. B. Scholkopf, S. Mika, C. J. C. Burges, P. Knirsch, K. R. Muller, G. Ratsch, "Input space versus feature space in kernel-based methods," IEEE Transactions on Neural Network, 1999, 10(5): 1000-1017
9. K. R. Muller, S. Mika, G. Ratsch, K. Tsuda, B. Scholkopf, "An introduction to kernel based learning algorithms," IEEE Transactions on Neural Networks, 2001, 12(2): 181-201
10. D.-N. Zheng, J.-X. Wang, Y.-N. Zhao, "Non-flat function estimation with a multi-scale support vector regession," Neurocomputing, 2006, 70 (1-3): 420-429
11. Z. Yang, J. Guo, W. Xu, X.-F. Nie, J. Wang, J.-J. Lei, "Multi-scale support vector machine for regression estimation," Advances in Neural Networks-ISNN 2006. Springer Berlin Heidelberg, 2006: 1030-1037
12. C. S. Ong, A. J. Smola, R. C. Williamson, "Learning the kernel with hyperkernels," The Journal of Machine Learning Research, 2005, 6(7): 1043-1071
13. N. Kingsbury, D. B. H. Tay, M. Palaniswami, "Multi-scale kernel methods for classification," Machine Learning for Signal Processing, 2005 IEEE Workshop on. IEEE, 2005: 43-48
14. H.-Q. Wang, F.-C. Sun, Y.-N. Cai, N. Chen, L.-G. Ding, "On Multiple Kernel Learning Methods," ACTA AUTOMATICA SINICA, 2010, 36 (8): 1037-1050
15. F. R. Bach, G. R. G. Lanckriet, M.-I. Jordan, "Multiple kernel learning, conic duality, and the SMO algorithm," Proceedings of the 21st International Conf. on Machine Learning. Banff, Canada: ACM, 2004: 6
16. S. Sonnenburg, G. Ratsch, C. Schafer, "A general and efficient multiple kernel learning algorithm," Proceedings of the Advances in Neural Information Processing Systems. Vancouver, Canada: The MIT Press, 2005: 1273-1280
17. A. Rakotomamonjy, F. Bach, S. Canu, Y. Grandvalet, "SimpleMKL," Journal of Machine Learning Research, 2008, 9: 2491-2521
18. G.-B. Huang, X. Ding, H. Zhou, "Optimization method based extreme learning machine for classification," Neurocomputing, 2010, 74(1): 155-163
19. P. L. Bartlett, "The sample complexity of pattern classification with neural networks: The size of the weights is more important than the size of the network," IEEE Trans. on Information Theory, 1998, 44(2): 525-536
20. R. Fletcher, "Practical Methods of Optimization: Volume 2 Constrained Optimization," Wiley, New York, 1981
21. G.-B. Huang, L. Chen, "Convex incremental extreme learning machine," Neurocomputing, 2007, 70(16): 3056-3062
22. G.-B. Huang, L. Chen, "Enhanced random search based incremental extreme learning machine," Neurocomputing, 2008, 71(16): 3460-3468
23. Q. Liu, Q. He, Z. Shi, "Extreme support vector machine classifier," Advances in Knowledge Discovery and Data Mining. Springer Berlin Heidelberg, 2008: 222-233
24. B. Frenay, M. Verleysen, "Using SVMs with randomised feature spaces: An extreme learning approach," Proceedings of The 18th European Symposium on Artificial Neural Networks (ESANN), Bruges, Belgium, 2010: 315-320
25. J. F. Bonnans, A. Shapiro. "Optimization problems with perturbations: A guided tour," SIAM review, 1998, 40(2): 228-264
26. O. Chapelle, V. Vapnik, O. Bousquet, S. Mukherjee, "Choosing multiple parameters for support vector machines," Machine learning, 2002, 46(1-3): 131-159
27. C. Lemarechal, C. Sagastizabal. "Practical Aspects of the Moreau-Yosida Regularization: Theoretical Preliminaries," SIAM Journal on Optimization, 1997, 7(2): 367-385
28. D. G. Luenberger, "Linear and nonlinear programming," Springer, 2003
29. J. F. Bonnans, F. Bonnans, "Optimisation continue: cours et problemes corriges," Dunod, 2006
30. D. DeCoste, K. Wagstaff, "Alpha seeding for support vector machines," Proceedings of the 6th ACM SIGKDD international conf. on Knowledge discovery and data mining. ACM, 2000: 345-349
31. C. L. Blake, C. J. Merz, "UCI Repository of Machine Learning Databases," Dept. Inf. Comput. Sci., Univ. California, Irvine, CA, 1998. Available: http://www. ics. uci. edu/-mlearn/MLRepository. html
32. E.-P. Xing, M. I. Jordan, R. M. Karp, "Feature selection for highdimensional genomic microarray data", ICML, 2001, 1: 601-608
33. S. Canu, Y. Grandvalet, V. Guigue, A. Rakotomamonjy, "SVM and kernel methods Matlab toolbox," LITIS EA4108, INSA de Rouen, France, 2003. [Online]. http://asi. insa-rouen. fr/enseignants/ arakotom/toolbox/ index. html
34. P. Ghanty, S. Paul, N. R. Pal, "NEUROSVM: An architecture to reduce the effect ofthe choice of kernel on the performance of svm," Journal of Machine Learning Research, 2009, 10: 591-622.