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Mathematical Problems in Engineering
Volume 2015 (2015), Article ID 923097, 13 pages
http://dx.doi.org/10.1155/2015/923097
Research Article

Distributed Learning over Massive XML Documents in ELM Feature Space

College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning 110819, China

Received 21 August 2014; Accepted 16 October 2014

Academic Editor: Tao Chen

Copyright © 2015 Xin Bi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. G. Salton and M. Mcgill, Introduction to Modern Information Retrieval, McGraw-Hill, New York, NY, USA, 1984.
  2. J. Yang and X. Chen, “A semi-structured document model for text mining,” Journal of Computer Science and Technology, vol. 17, no. 5, pp. 603–610, 2002. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  3. X.-G. Zhao, G. Wang, X. Bi, P. Gong, and Y. Zhao, “XML document classification based on ELM,” Neurocomputing, vol. 74, no. 16, pp. 2444–2451, 2011. View at Publisher · View at Google Scholar · View at Scopus
  4. X. Zhao, X. Bi, and B. Qiao, “Probability based voting extreme learning machine for multiclass XML documents classification,” World Wide Web, pp. 1–15, 2013. View at Publisher · View at Google Scholar · View at Scopus
  5. G. B. Huang, Q. Y. Zhu, and C. K. Siew, “Extreme learning machine: a new learning scheme of feedforward neural networks,” in Proceedings of the IEEE International Joint Conference on Neural Networks, vol. 2, pp. 985–990, July 2004. View at Publisher · View at Google Scholar · View at Scopus
  6. 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, 2006. View at Publisher · View at Google Scholar · View at Scopus
  7. G.-B. Huang, Q.-Y. Zhu, K. Z. Mao, C.-K. Siew, P. Saratchandran, and N. Sundararajan, “Can threshold networks be trained directly?” IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 53, no. 3, pp. 187–191, 2006. View at Publisher · View at Google Scholar · View at Scopus
  8. G. Feng, G.-B. Huang, Q. Lin, and R. K. L. Gay, “Error minimized extreme learning machine with growth of hidden nodes and incremental learning,” IEEE Transactions on Neural Networks, vol. 20, no. 8, pp. 1352–1357, 2009. View at Publisher · View at Google Scholar · View at Scopus
  9. H.-J. Rong, G.-B. Huang, N. Sundararajan, and P. Saratchandran, “Online sequential fuzzy extreme learning machine for function approximation and classification problems,” IEEE Transactions on Systems, Man, and Cybernetics B: Cybernetics, vol. 39, no. 4, pp. 1067–1072, 2009. View at Publisher · View at Google Scholar · View at Scopus
  10. G.-B. Huang and L. Chen, “Enhanced random search based incremental extreme learning machine,” Neurocomputing, vol. 71, no. 16–18, pp. 3460–3468, 2008. View at Publisher · View at Google Scholar · View at Scopus
  11. G.-B. Huang and L. Chen, “Convex incremental extreme learning machine,” Neurocomputing, vol. 70, no. 16-18, pp. 3056–3062, 2007. View at Publisher · View at Google Scholar · View at Scopus
  12. X.-G. Zhao, G. Wang, X. Bi, P. Gong, and Y. Zhao, “XML document classification based on ELM,” Neurocomputing, vol. 74, no. 16, pp. 2444–2451, 2011. View at Publisher · View at Google Scholar · View at Scopus
  13. B. Lu, G. Wang, Y. Yuan, and D. Han, “Semantic concept detection for video based on extreme learning machine,” Neurocomputing, vol. 102, pp. 176–183, 2013. View at Publisher · View at Google Scholar · View at Scopus
  14. Y. Lan, Z. Hu, Y. C. Soh, and G.-B. Huang, “An extreme learning machine approach for speaker recognition,” Neural Computing and Applications, vol. 22, no. 3-4, pp. 417–425, 2013. View at Publisher · View at Google Scholar · View at Scopus
  15. W. Zong and G.-B. Huang, “Face recognition based on extreme learning machine,” Neurocomputing, vol. 74, no. 16, pp. 2541–2551, 2011. View at Publisher · View at Google Scholar · View at Scopus
  16. G. Wang, Y. Zhao, and D. Wang, “A protein secondary structure prediction framework based on the extreme learning machine,” Neurocomputing, vol. 72, no. 1–3, pp. 262–268, 2008. View at Publisher · View at Google Scholar · View at Scopus
  17. B. Wang, G. Wang, J. Li, and B. Wang, “Update strategy based on region classification using ELM for mobile object index,” Soft Computing, vol. 16, no. 9, pp. 1607–1615, 2012. View at Publisher · View at Google Scholar · View at Scopus
  18. G.-B. Huang, X. Ding, and H. Zhou, “Optimization method based extreme learning machine for classification,” Neurocomputing, vol. 74, no. 1–3, pp. 155–163, 2010. View at Publisher · View at Google Scholar · View at Scopus
  19. C. Cortes and V. Vapnik, “Support-vector networks,” Machine Learning, vol. 20, no. 3, pp. 273–297, 1995. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  20. G.-B. Huang, H. Zhou, X. Ding, and R. Zhang, “Extreme learning machine for regression and multiclass classification,” IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 42, no. 2, pp. 513–529, 2012. View at Publisher · View at Google Scholar · View at Scopus
  21. G.-B. Huang, L. Chen, and C.-K. Siew, “Universal approximation using incremental constructive feedforward networks with random hidden nodes,” IEEE Transactions on Neural Networks, vol. 17, no. 4, pp. 879–892, 2006. View at Publisher · View at Google Scholar · View at Scopus
  22. G. Huang, S. Song, J. N. D. Gupta, and C. Wu, “Semi-supervised and unsupervised extreme learning machines,” IEEE Transactions on Cybernetics, 2014. View at Publisher · View at Google Scholar · View at Scopus
  23. Q. He, X. Jin, C. Du, F. Zhuang, and Z. Shi, “Clustering in extreme learning machine feature space,” Neurocomputing, vol. 128, pp. 88–95, 2014. View at Publisher · View at Google Scholar · View at Scopus
  24. T. Ngo, “Data mining: practical machine learning tools and technique,” in ACM Sigsoft Software Engineering Notes, I. H. Witten, E. Frank, and M. A. Hell, Eds., pp. 51–52, 3rd edition, 2011. View at Google Scholar
  25. J. Dean and S. Ghemawat, “MapReduce: simplied data processing on large clusters,” in Operating Systems Design and Implementation, pp. 137–150, 2004. View at Google Scholar
  26. Q. He, T. Shang, F. Zhuang, and Z. Shi, “Parallel extreme learning machine for regression based on mapReduce,” Neurocomputing, vol. 102, pp. 52–58, 2013. View at Publisher · View at Google Scholar · View at Scopus
  27. B. Wang, S. Huang, J. Qiu, Y. Liu, and G. Wang, “Parallel online sequential extreme learning machine based on MapReduce,” Neurocomputing, 2014. View at Publisher · View at Google Scholar
  28. G. Salton and C. Buckley, “Term-weighting approaches in automatic text retrieval,” Information Processing & Management, vol. 24, no. 5, pp. 513–523, 1988. View at Publisher · View at Google Scholar · View at Scopus
  29. A. E. Hoerl and R. W. Kennard, “Ridge regression: biased estimation for nonorthogonal problems,” Technometrics, vol. 12, no. 1, pp. 55–67, 1970. View at Publisher · View at Google Scholar