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1608 JOURNAL OF COMPUTERS, VOL. 8, NO. 6, JUNE 2013 In this paper, we first extended the traditional kernelbased learning problem to multiclass cases in an Extreme Learning Machine context. To enhance the performance of ELM, a deformed kernel was proposed, which can make use of underlying information from both labeled and unlabeled samples. To speed up our algorithm, we further approximated the deformed kernel by means of random feature mapping. Our algorithm does not need kernel parameter tuning. The experimental results have shown that the DKELM algorithm achieves better generalization performance at a relatively faster learning speed than traditional semi-supervised classification algorithms. In the future, we will further optimize our proposed framework and study the sparse regularization problems in our framework. ACKNOWLEDGMENTS This work was supported by the National Natural Science Foundation of China under Grant Nos. 50674086. REFERENCES [1] X. Tang and M. Han, “Partial Lanczos extreme learning machine for single-output regression problems,” Neurocomputing, vol. 72, no. 13–15, pp. 3066–3076, Aug. 2009. [2] Q. Liu, Q. He, and Z.-Z. Shi, “Extreme support vector machine classifier,” Lecture Notes in Computer Science, vol. 5012, pp. 222–233, 2008. [3] B. Frénay and M. Verleysen, “Using SVMs with randomised feature spaces: An extreme learning approach,” in Proc. 18th ESANN, Bruges, Belgium, Apr. 28–30, pp. 315–320,2010. [4] Y. Miche, A. Sorjamaa, P. Bas, O. Simula, C. Jutten, and A. Lendasse, “OP-ELM: Optimally pruned extreme learning machine,” IEEE Trans. Neural Netw., vol. 21, no. 1, pp. 158–162, Jan. 2010. [5] W. Deng, Q. Zheng, and L. Chen, “Regularized extreme learning machine,” in Proc. IEEE Symp. CIDM, Mar. 30– Apr. 2, pp. 389–395,2009. [6] G.-B. Huang, Q.-Y. Zhu, and C.-K. Siew, “Extreme learning machine: A new learning scheme of feedforward neural networks,” in Proc. IJCNN, Budapest, Hungary, Jul. 25–29, vol. 2, pp. 985–990.,2004. [7] G.-B. Huang, Q.-Y. Zhu, and C.-K. Siew, “Extreme learning machine: Theory and applications,” Neurocomputing, vol. 70, no. 1–3, pp. 489–501, Dec. 2006. [8] G.-B. Huang, L. Chen, and C.-K. Siew, “Universal approximation using incremental constructive feedforward networks with random hidden nodes,” IEEE Trans. Neural Netw., vol. 17, no. 4, pp. 879–892, Jul. 2006. [9] G.-B. Huang and L. Chen, “Convex incremental extreme learning ma-chine,” Neurocomputing, vol. 70, no. 16–18, pp. 3056–3062, Oct. 2007. [10] G.-B. Huang and L. Chen, “Enhanced random search based incremental extreme learning machine,” Neurocomputing, vol. 71, no. 16–18, pp. 3460–3468, Oct. 2008. [11] D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning representations by back-propagation errors,” Nature, vol. 323, pp. 533–536, 1986. [12] T. Joachims. Transductive inference for text classification using support vector machines. In ICML 16, pp:200-209, 1999. [13] K. P. Bennett and A. Demiriz. Semi-supervised support vector machines. In NIPS, pages 368-374, 1999. [14] O. Chapelle, V. Sindhwani, and S. S. Keerthi. Branch and bound for semi-supervised support vector machines. InNIPS, pages 217-224, 2006. [15] O. Chapelle and A. Zien. Semi-supervised classification by low density separation. In AISTATS 10, 2005. [16] O. Chapelle, B. Scholkopf, and A. Zien. Semi-Supervised Learning. MIT Press: Cambridge, MA, 2006. [17] L. Xu, J. Neufeld, B. Larson, and D. Schuurmans. Maximum margin clustering. In NIPS, 2004. [18] L. Xu and D. Schuurmans. Unsupervised and semisupervised multi-class support vector machines. In AAAI, 2005. [19] Z. Xu, R. Jin, J. Zhu, I. King, and M. Lyu. Efficient convex relaxation for transductive support vector machine. In NIPS, 2007. [20] O. Chapelle, M. Chi, and A. Zien. A continuation method for semi-supervised svms. In ICML 23, pages 185- 192,2006. [21] A. Astorino and A. Fuduli. Nonsmooth optimization techniques for semi-supervised classification. IEEE Trans.Pattern Anal. Mach. Intell., 29(12):2135-2142, 2007. [22] R. Collobert, F. Sinz, J. Weston, and L. Bottou. Large scale transductive svms. Journal of Machine Learning Research, 7:1687-1712, 2006. [23] G. Fung and O. Mangasarian. Semi-supervised support vector machines for unlabeled data classification. Optimization Methods and Software, 15:29-44, 2001. [24] V. Sindhwani, S. S. Keerthi, and O. Chapelle.Deterministic annealing for semi-supervised kernel machines. In ICML 23, pp: 841-848, 2006. [25] Bin Zhao, Fei Wang, Changshui Zhang.CutS3VM: A Fast Semi-Supervised SVM Algorithm.The 14th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining(KDD).pp:830-838,August 24- 27,2008. [26] V. Sindhwani, S.S. Keerthi. Large Scale Semi-supervised Linear SVMs. 29th Annual International ACM SIGIR, Technical report, 2006. [27] M. Belkin, V. Sindhwani, P.Niyogi, Manifold regularization: a geometric framework for learning from labeled and unlabeled examples, J. Mach. Learn. Res. 7:2399–2434,2006. [28] H. Xue, S. Chen, Q. Yang, Discriminatively regularized least-squares classification, Pattern Recognition 42(1) pp:93–104,2009. [29] Mingyu Fan, Nannan Gu, Hong Qiao, etc. Sparse regularization for semi-supervised classification .Pattern Recognition, 44(8,), pp: 1777-1784,2011 [30] G.-B. Huang, H.-M Zhou, X.-J. Ding. Extreme Learning Machine for Regression and Multiclass Classification IEEE Transactions on Systems, Man, and Cybernetics- PART B: Cybernetics, Vol. 42, no. 2, 513 – 529,2012. [31] Huang et al. Extreme learning machines: a survey .International Journal of Machine Learning and Cybernetics. pp:107–122,2011. © 2013 ACADEMY PUBLISHER
JOURNAL OF COMPUTERS, VOL. 8, NO. 6, JUNE 2013 1609 Zhang Chen is current a Ph.D candidate at China University of Mining and Technology(CUMT), China. She received her MS degree in Computer Application Technology from CUMT in 2004, and her BS degree in Computer Science from CUMT in 2001. She is currently a lecture at school of Computer Science and Technology, CUMT. Her research interest is computation intelligence and machine learning et al. Xia Shi Xiong is born in 1962, Ph.D. He is a professor at school of Computer Science and Technology in CUMT. He has published more than 60 research papers in journals and international conferences. His research interest is Wireless sensor networks and intelligent information processing et al. Liu Bing is current a Ph.D candidate at China University of Mining and Technology(CUMT), China. She received her MS degree in Computer Application Technology from CUMT in 2005, and her BS degree in Computer Science from CUMT in 2002. She is currently a lecture at school of Computer Science and Technology, CUMT. His research interest is computation intelligence and machine learning et al. © 2013 ACADEMY PUBLISHER
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Corn Moisture Measurement using a C
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Page 261: Call for Papers and Special Issues
Page 264: (Contents Continued from Back Cover
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