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A Preliminary Study on Transductive Extreme Learning Machines

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Recent Advances of Neural Network Models and Applications

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 26))

Abstract

Transductive learning is the problem of designing learning machines that succesfully generalize only on a given set of input patterns. In this paper we begin the study towards the extension of Extreme Learning Machine (ELM) theory to the transductive setting, focusing on the binary classification case. To this end, we analyze previous work on Transductive Support Vector Machines (TSVM) learning, and introduce the Transductive ELM (TELM) model. Contrary to TSVM, we show that the optimization of TELM results in a purely combinatorial search over the unknown labels. Some preliminary results on an artifical dataset show substained improvements with respect to a standard ELM model.

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Author information

Authors and Affiliations

  1. Department of Information Engineering, Electronics and Telecommunications (DIET), “Sapienza” University of Rome, via Eudossiana 18, 00184, Rome, Italy

    Simone Scardapane, Danilo Comminiello, Michele Scarpiniti & Aurelio Uncini

Authors
  1. Simone Scardapane
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  2. Danilo Comminiello
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  3. Michele Scarpiniti
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  4. Aurelio Uncini
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Corresponding author

Correspondence to Simone Scardapane .

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Milano, Milano, Italy

    Simone Bassis

  2. Department of Psychology, Second University of Naples, Institute for Advanced Scientific Studies (IIASS), Caserta, Italy

    Anna Esposito

  3. Department of Civil, Energy, Environmental, and Materials Engineering, DICEAM, University Mediterranea of Reggio Calabria, Reggio Calabria, Italy

    Francesco Carlo Morabito

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© 2014 Springer International Publishing Switzerland

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Scardapane, S., Comminiello, D., Scarpiniti, M., Uncini, A. (2014). A Preliminary Study on Transductive Extreme Learning Machines. In: Bassis, S., Esposito, A., Morabito, F. (eds) Recent Advances of Neural Network Models and Applications. Smart Innovation, Systems and Technologies, vol 26. Springer, Cham. https://doi.org/10.1007/978-3-319-04129-2_3

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  • DOI: https://doi.org/10.1007/978-3-319-04129-2_3

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-04128-5

  • Online ISBN: 978-3-319-04129-2

  • eBook Packages: EngineeringEngineering (R0)

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