Accelerating ELM training over data streams

Ji, Hangxu; Wu, Gang; Wang, Guoren

doi:10.1007/s13042-020-01158-8

Cited by 1 publication

(1 citation statement)

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“…The proposed method provides an efficient solution to multi-label classification with large-scale data sets. In the same year, an efficient streambased distributed framework for ELM and OS-ELM was proposed by Ji et al [110]. The proposed model achieves better performance concerning offline and online training in different data arrival modes and user needs.…”

Section: Information Not Providedmentioning

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: Information Not Providedmentioning

confidence: 99%