Extreme Learning Regression for nu Regularization

References

• Bartlett, P. L. 1998. The sample complexity of pattern classification with neural networks: The size of the weights is more important than the size of the network. IEEE Transactions on Information Theory 44 (2):525–36. doi:10.1109/18.661502.
   Web of Science ®Google Scholar
• Blake, C. K., and C. J. Merz. 2013. UCI repository of machine learning databases. http://archive.ics.uci.edu/ml/.
   Google Scholar
• Bu, Z., J. Li, C. Zhang, J. Cao, A. Li, and Y. Shi. 2019. Graph K-means based on leader identification, dynamic game, and opinion dynamics. IEEE Transactions on Knowledge and Data Engineering. doi:10.1109/TKDE.2019.2903712.
   Web of Science ®Google Scholar
• Chang, C. C., and C. J. Lin. 2002. Training v-support vector regression: theory and algorithms. Neural Computation 14 (8):1959–77. doi:10.1162/089976602760128081.
   PubMed Web of Science ®Google Scholar
• Cortes, C., and V. Vapnik. 1995. Support vector networks. Machine Learning 20:273–97. doi:10.1007/BF00994018.
   Web of Science ®Google Scholar
• Ding, S., N. Zhang, J. Zhang, X. Xu, Z. Shi. 2017. Unsupervised extreme learning machine with representational features. International Journal of Machine Learning and Cybernetics. 8(2):587–95. doi:10.1007/s13042-015-0351-8.
   Web of Science ®Google Scholar
• Ding, X., and B. Chang. 2014. Active set strategy of optimized extreme learning machine. Chinese Science Bulletin 59 (31):4152–60. doi:10.1007/s11434-014-0512-2.
   Google Scholar
• Ding, X. J., Y. Lan, Z. F. Zhang, and X. Xu. 2017. Optimization extreme learning machine with ν regularization. Neurocomputing 261:11–19. doi:10.1016/j.neucom.2016.05.114.
   Web of Science ®Google Scholar
• Fletcher, R. 1981. Practical Methods of Optimization: Volume2 Constrained Optimization. New York: Wiley.
   Google Scholar
• Frénay, B., and M. Verleysen. 2010. Using SVMs with randomized feature spaces: An extreme learning approach, in: Proceedings of The 18th European Symposium on Artificial Neural Networks (ESANN), Bruges, Belgium, 28–30 April, 2010, 315–20.
   Google Scholar
• Ghanty, P., S. Paul, and N. R. Pal. 2009. NEUROSVM: An architecture to reduce the effect of the choice of kernel on the performance of svm. Journal of Machine Learning Research 10:591–622.
   Web of Science ®Google Scholar
• He, Q., J. Xin, C. Y. Du, F. Zhuang, Z. Shi. 2014. Clustering in extreme learning machine feature space. Neurocomputing 128:88–95. doi:10.1016/j.neucom.2012.12.063.
   Web of Science ®Google Scholar
• Huang, G.-B., L. Chen, and C.-K. Siew. 2006. Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Transactions on Neural Networks 17 (4):879–92. doi:10.1109/TNN.2006.875977.
   PubMed Web of Science ®Google Scholar
• Huang, G.-B., X. Ding, and H. Zhou. 2010. Optimization method based extreme learning machine for classification. Neurocomputing 74:155–63. doi:10.1016/j.neucom.2010.02.019.
   Web of Science ®Google Scholar
• Huang, G.-B., H. Zhou, X. Ding, and R. Zhang. 2012. Extreme learning machine for regression and multiclass classification. IEEE Transaction on System, Man, and Cybernetics-Part B: Cybernetics 42 (2):513–29. doi:10.1109/TSMCB.2011.2168604.
   PubMed Web of Science ®Google Scholar
• Huang, G.-B., Q.-Y. Zhu, K.-Z. Mao, C.-K. Siew, P. Saratchandran, and N. Sundararajan. 2006. Can threshold networks be trained directly? IEEE Transactions on Circuits and Systems-II: Express Briefs 53 (3):187–91. doi:10.1109/TCSII.2005.857540.
   Web of Science ®Google Scholar
• Huang, G.-B., Q.-Y. Zhu, and C.-K. Siew. 2006. Extreme learning machine: Theory and applications. Neurocomputing 70:489–501. doi:10.1016/j.neucom.2005.12.126.
   Web of Science ®Google Scholar
• Lauren, P., G. Qu, J. Yang, P. Watta, G-B. Huang, A. Lendasse. 2018. Generating word embeddings from an extreme learning machine for sentiment analysis and sequence labeling tasks. Cognitive Computation 10(4):625–38. doi:10.1007/s12559-018-9548-y.
   Web of Science ®Google Scholar
• Mike, M. 2005. Statistical datasets. http://lib.stat.cmu.edu/datasets/.
   Google Scholar
• Park, J., and I. W. Sandberg. 1991. Universal approximation using radial basis-function networks. Neural Computation 3:246–57. doi:10.1162/neco.1991.3.2.246.
   PubMed Web of Science ®Google Scholar
• Schölkopf, B., A. Smola, R. C. Williamson, and P. L. Bartlett. 2000. New support vector algorithms. Neural Computation 12:1207–45. doi:10.1162/089976600300015565.
   PubMed Web of Science ®Google Scholar
• Shin, Y., and J. Ghosh. 1995. Ridge polynomial networks. IEEE Transactions on Neural Networks 6 (3):610–22. doi:10.1109/72.377967.
   PubMedGoogle Scholar
• Zhang, Q., and A. Benveniste. 1992. Wavelet networks. IEEE Transactions on Neural Networks 3 (6):889–98. doi:10.1109/72.165591.
   PubMed Web of Science ®Google Scholar