Extreme learning machine with a deterministic assignment of hidden weights in two parallel layers

Pablo A. Henríquez, Gonzalo A. Ruz

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

Extreme learning machine (ELM) is a machine learning technique based on competitive single-hidden layer feedforward neural network (SLFN). However, traditional ELM and its variants are only based on random assignment of hidden weights using a uniform distribution, and then the calculation of the weights output using the least-squares method. This paper proposes a new architecture based on a non-linear layer in parallel by another non-linear layer and with entries of independent weights. We explore the use of a deterministic assignment of the hidden weight values using low-discrepancy sequences (LDSs). The simulations are performed with Halton and Sobol sequences. The results for regression and classification problems confirm the advantages of using the proposed method called PL-ELM algorithm with the deterministic assignment of hidden weights. Moreover, the PL-ELM algorithm with the deterministic generation using LDSs can be extended to other modified ELM algorithms.

Original languageEnglish
Pages (from-to)109-116
Number of pages8
JournalNeurocomputing
Volume226
DOIs
StatePublished - 22 Feb 2017

Keywords

  • Classification
  • Extreme learning machine
  • Low-discrepancy points
  • Parallel layers
  • Regression

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