Extreme Learning Classifier with Deep Concepts

  • Bernardete Ribeiro
  • Noel Lopes
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8258)


The text below describes a short introduction to extreme learning machines (ELM) enlightened by new developed applications. It also includes an introduction to deep belief networks (DBN), noticeably tuned into the pattern recognition problems. Essentially, the deep belief networks learn to extract invariant characteristics of an object or, in other words, an DBN shows the ability to simulate how the brain recognizes patterns by the contrastive divergence algorithm. Moreover, it contains a strategy based on both the kernel (and neural) extreme learning of the deep features. Finally, it shows that the DBN-ELM recognition rate is competitive (and often better) than other successful approaches in well-known benchmarks. The results also show that the method is extremely fast when the neural based ELM is used.


Extreme Learning Machines Deep learning Neural Networks 


  1. 1.
    Bengio, Y.: Learning deep architectures for AI. Foundations and Trends in Machine Learning 2(1), 1–127 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Hinton, G.: A practical guide to training Restricted Boltzmann Machines. Tech. rep., Dep. of Computer Science, University of Toronto (2010)Google Scholar
  3. 3.
    Hinton, G.E., Osindero, S., Teh, Y.-W.: A fast learning algorithm for deep belief nets. Neural Comput. 18, 1527–1554 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Huang, G.-B., Zhou, H., Ding, X., Zhang, R.: Extreme learning machine for regression and multiclass classification. IEEE Transactions on Systems, Man, and Cybernetics, Part B 42(2), 513–529 (2012)CrossRefGoogle Scholar
  5. 5.
    Huang, G.-B., Zhu, Q.-Y., Siew, C.-K.: Extreme learning machine: A new learning scheme of feedforward neural networks. In: IEEE International Joint Conference on Neural Networks, pp. 985–990 (2004)Google Scholar
  6. 6.
    Huang, G.-B., Zhu, Q.-Y., Siew, C.-K.: Extreme learning machine: Theory and applications. Neurocomputing 70, 489–501 (2006)CrossRefGoogle Scholar
  7. 7.
    Le Roux, N., Bengio, Y.: Representational power of restricted boltzmann machines and deep belief networks. Neural Comput. 20, 1631–1649 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Lopes, N., Ribeiro, B.: GPUMLib: An efficient open-source GPU machine learning library. International Journal of Computer Information Systems and Industrial Management Applications 3, 355–362 (2011)Google Scholar
  9. 9.
    Lu, B., Wang, G., Yuan, Y., Han, D.: Semantic concept detection for video based on extreme learning machine. Neurocomputing 102, 176–183 (2013)CrossRefGoogle Scholar
  10. 10.
    Ranzato, M., Boureau, Y., LeCun, Y.: Sparse feature learning for deep belief networks. In: Advances in Neural Information Processing Systems, vol. 20 (2007)Google Scholar
  11. 11.
    Shi, L.C., Lu, B.L.: EEG-based vigilance estimation using extreme learning machines. Neurocomputing 102, 135–143 (2013)CrossRefGoogle Scholar
  12. 12.
    Wu, S., Wang, Y., Cheng, S.: Extreme learning machine based wind speed estimation and sensorless control for wind turbine power generation system. Neurocomputing 102, 163–175 (2013)CrossRefGoogle Scholar
  13. 13.
    Yeu, C.W., Lim, M.H., Huang, G.B., Agarwal, A., Ong, Y.S.: A new machine learning paradigm for terrain reconstruction. IEEE Geoscience and Remote Sensing Letters 3(3), 382–386 (2006)CrossRefGoogle Scholar
  14. 14.
    Yu, K., Xu, W., Gong, Y.: Deep learning with kernel regularization for visual recognition. In: Neural Information Processing Systems, NIPS (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Bernardete Ribeiro
    • 1
  • Noel Lopes
    • 1
    • 2
  1. 1.CISUC - Department of Informatics EngineeringUniversity of CoimbraPortugal
  2. 2.UDI/IPG - Research UnitPolytechnic Institute of GuardaPortugal

Personalised recommendations