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Applying Least Angle Regression to ELM

  • Hang Shao
  • Nathalie Japkowicz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7310)

Abstract

Basic extreme learning machines apply least square solution to calculate the neural network’s output weights. In the presence of outliers and multi-collinearity, the least square solution becomes invalid. In order to fix this problem, a new kind of extreme learning machine is proposed. An outlier detection technique is introduced to locate outliers and avoid their interference. The least square solution is replaced by regularization for output weights calculation during which the number of hidden nodes is also automatically chosen. Simulation results show that the proposed model has good prediction performance on both normal datasets and datasets contaminated by outliers.

Keywords

Machine learning Extreme learning machine Least angle regression Outlier detection Robustness 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Hang Shao
    • 1
  • Nathalie Japkowicz
    • 1
  1. 1.School of Electrical and Computer EngineeringUniversity of OttawaOttawaCanada

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