A Preliminary Study on Transductive Extreme Learning Machines

  • Simone Scardapane
  • Danilo Comminiello
  • Michele Scarpiniti
  • Aurelio Uncini
Part of the Smart Innovation, Systems and Technologies book series (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.

Keywords

Transductive learning extreme learning machine semi-supervised learning 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Simone Scardapane
    • 1
  • Danilo Comminiello
    • 1
  • Michele Scarpiniti
    • 1
  • Aurelio Uncini
    • 1
  1. 1.Department of Information Engineering, Electronics and Telecommunications (DIET)“Sapienza” University of RomeRomeItaly

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