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Author  |
Henriquez, P.A.; Ruz, G.A. |
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Title |
Extreme learning machine with a deterministic assignment of hidden weights in two parallel layers |
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Year |
2017 |
Publication |
Neurocomputing |
Abbreviated Journal |
Neurocomputing |
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Volume |
226 |
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Pages |
109-116 |
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Keywords |
Extreme learning machine; Low-discrepancy points; Parallel layers; Regression; Classification |
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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. |
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Address |
[Henriquez, Pablo A.; Ruz, Gonzalo A.] Univ Adolfo Ibanez, Fac Ingn & Ciencias, Ave Diagonal Las Torres 2640, Santiago, Chile, Email: pabhenriquez@alumnos.uai.cl; |
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Publisher |
Elsevier Science Bv |
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Language |
English |
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ISSN |
0925-2312 |
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Notes |
WOS:000392037800012 |
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Call Number |
UAI @ eduardo.moreno @ |
Serial |
687 |
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