A weighted voting method using minimum square error based on extreme learning machine

Proceedings - International Conference on Machine Learning and Cybernetics

Volumn 1, Issue , 2012, Pages 411-414

  Cao, Jing Jing ^a   Kwong, Sam ^a   Wang, Ran ^a   Li, Ke ^a  

^a CITY UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

Extreme learning machine; Minimum square error; Weighted voting

Indexed keywords

CLASSIFICATION PERFORMANCE; DATA SETS; EXTREME LEARNING MACHINE; HIDDEN NODES; LINEAR COMBINATIONS; MINIMUM SQUARE ERRORS; MULTIPLE CLASSIFIERS; RANDOM INPUT; WEIGHTED VOTING;

CLASSIFICATION (OF INFORMATION); CYBERNETICS; KNOWLEDGE ACQUISITION;

LEARNING SYSTEMS;

EID: 84871653301     PISSN: 2160133X     EISSN: 21601348     Source Type: Conference Proceeding    
DOI: 10.1109/ICMLC.2012.6358949     Document Type: Conference Paper

References (12)

1. J.A. Benediktsson, J.R. Sveinsson, O.K. Ersoy, and P.H. Swain. Parallel consensual neural networks. Neural Networks, IEEE Transactions on, 8(1):54-64, 1997.
2. Jiuwen Cao, Zhiping Lin, Guang-Bin Huang, and Nan Liu. Voting based extreme learning machine. Inf. Sci., 185(1):66-77, 2012.
3. M.T. Hagan, H.B. Demuth, M.H. Beale, and Boulder University of Colorado. Neural network design. PWS P ub, 1996.
4. G.B. Huang, L. Chen, and C.K. Siew. Universal approximation using incremental constructive feedforward networks with random hidden nodes. Neural Networks, IEEE Transactions on, 17(4):879-892, 2006.
5. G.B. Huang, Q.Y. Zhu, and C.K. Siew. Extreme learning machine: theory and applications. Neurocomputing, 70(1):489-501, 2006.
6. Y. Lan, Y.C. Soh, and G.B. Huang. Ensemble of online sequential extreme learning machine. Neurocomputing, 72(13-15):3391-3395, 2009.
7. N.Y. Liang, G.B. Huang, P. Saratchandran, and N. Sundararajan. A fast and accurate online sequential learning algorithm for feedforward networks. Neural Networks, IEEE Transactions on, 17(6):1411-1423, 2006.
8. N. Liu and H. Wang. Ensemble based extreme learning machine. Signal Processing Letters, IEEE, 17(8):754-757, 2010.
9. D. Serre. Matrices: Theory and applications. 2002.
10. A. Tan, D. Gilbert, and Y. Deville. Multi-class protein fold classification using a new ensemble machine learning approach. 2003.
11. N. Ueda. Optimal linear combination of neural networks for improving classification performance. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 22(2):207-215, 2000.
12. L. Yang, R. Jin, L. Mummert, R. Sukthankar, A. Goode, B. Zheng, S.C.H. Hoi, and M. Satyanarayanan. A boosting framework for visuality-preserving distance metric learning and its application to medical image retrieval. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 32(1):30-44, 2010.