Extreme Learning Machine for Business Sales Forecasts: A Systematic Review

Saldaña-Olivas, Edu; Huamán-Tuesta, José Roberto

doi:10.1007/978-3-030-57548-9_8

Cited by 1 publication

(1 citation statement)

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“…In Saldaña-Olivas and Huamán-Tuesta [23], a systematic review was carried out to identify the level of help that an ELM network provides to companies' sales forecasts. The authors conclude that ELM models greatly improve the accuracy of sales forecasts compared to traditional techniques, so they suggest further research and studies with real data.…”

Section: Elm Based On Metaheuristicsmentioning

confidence: 99%

A Review on Large-Scale Data Processing with Parallel and Distributed Randomized Extreme Learning Machine Neural Networks

Gelvez-Almeida,

Mora,

Barrientos

et al. 2024

MCA

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The randomization-based feedforward neural network has raised great interest in the scientific community due to its simplicity, training speed, and accuracy comparable to traditional learning algorithms. The basic algorithm consists of randomly determining the weights and biases of the hidden layer and analytically calculating the weights of the output layer by solving a linear overdetermined system using the Moore–Penrose generalized inverse. When processing large volumes of data, randomization-based feedforward neural network models consume large amounts of memory and drastically increase training time. To efficiently solve the above problems, parallel and distributed models have recently been proposed. Previous reviews of randomization-based feedforward neural network models have mainly focused on categorizing and describing the evolution of the algorithms presented in the literature. The main contribution of this paper is to approach the topic from the perspective of the handling of large volumes of data. In this sense, we present a current and extensive review of the parallel and distributed models of randomized feedforward neural networks, focusing on extreme learning machine. In particular, we review the mathematical foundations (Moore–Penrose generalized inverse and solution of linear systems using parallel and distributed methods) and hardware and software technologies considered in current implementations.

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Section: Elm Based On Metaheuristicsmentioning

confidence: 99%