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Henriquez, P. A., & Ruz, G. A. (2017). Extreme learning machine with a deterministic assignment of hidden weights in two parallel layers. Neurocomputing, 226, 109–116.
Abstract: Extreme learning machine (ELM) is a machine learning technique based on competitive singlehidden 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 leastsquares method. This paper proposes a new architecture based on a nonlinear layer in parallel by another nonlinear layer and with entries of independent weights. We explore the use of a deterministic assignment of the hidden weight values using lowdiscrepancy 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 PLELM algorithm with the deterministic assignment of hidden weights. Moreover, the PLELM algorithm with the deterministic generation using LDSs can be extended to other modified ELM algorithms.

Henriquez, P. A., & Ruz, G. A. (2019). Noise reduction for nearinfrared spectroscopy data using extreme learning machines. Eng. Appl. Artif. Intell., 79, 13–22.
Abstract: The near infrared (NIR) spectra technique is an effective approach to predict chemical properties and it is typically applied in petrochemical, agricultural, medical, and environmental sectors. NIR spectra are usually of very high dimensions and contain huge amounts of information. Most of the information is irrelevant to the target problem and some is simply noise. Thus, it is not an easy task to discover the relationship between NIR spectra and the predictive variable. However, this kind of regression analysis is one of the main topics of machine learning. Thus machine learning techniques play a key role in NIR based analytical approaches. Preprocessing of NIR spectral data has become an integral part of chemometrics modeling. The objective of the preprocessing is to remove physical phenomena (noise) in the spectra in order to improve the regression or classification model. In this work, we propose to reduce the noise using extreme learning machines which have shown good predictive performances in regression applications as well as in large dataset classification tasks. For this, we use a novel algorithm called CPLELM, which has an architecture in parallel based on a nonlinear layer in parallel with another nonlinear layer. Using the soft margin loss function concept, we incorporate two Lagrange multipliers with the objective of including the noise of spectral data. Six reallife dataset were analyzed to illustrate the performance of the developed models. The results for regression and classification problems confirm the advantages of using the proposed method in terms of root mean square error and accuracy.
