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Author Henriquez, P.A.; Ruz, G.A. pdf  doi
  Title Extreme learning machine with a deterministic assignment of hidden weights in two parallel layers Type
  Year 2017 Publication Neurocomputing Abbreviated Journal Neurocomputing  
  Volume 226 Issue Pages 109-116  
  Keywords Extreme learning machine; Low-discrepancy points; Parallel layers; Regression; Classification  
  Abstract Extreme learning machine (ELM) is a machine learning technique based on competitive single-hidden layer feedforward neural network (SLFN). However, traclitional 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.  
  Address [Henriquez, Pablo A.; Ruz, Gonzalo A.] Univ Adolfo Ibanez, Fac Ingn & Ciencias, Ave Diagonal Las Torres 2640, Santiago, Chile, Email:;  
  Corporate Author Thesis  
  Publisher Elsevier Science Bv Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0925-2312 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000392037800012 Approved  
  Call Number UAI @ eduardo.moreno @ Serial 687  
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