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Extreme Learning Classifier with Deep Concepts

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

Abstract

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.

Keywords

Extreme Learning Machines Deep learning Neural Networks 

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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

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