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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 3-4/2013

01.03.2013 | Extreme Learning Machine's Theory & Application

A modified extreme learning machine with sigmoidal activation functions

verfasst von: Zhixiang X. Chen, Houying Y. Zhu, Yuguang G. Wang

Erschienen in: Neural Computing and Applications | Ausgabe 3-4/2013

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Abstract

This paper proposes a modified ELM algorithm that properly selects the input weights and biases before training the output weights of single-hidden layer feedforward neural networks with sigmoidal activation function and proves mathematically the hidden layer output matrix maintains full column rank. The modified ELM avoids the randomness compared with the ELM. The experimental results of both regression and classification problems show good performance of the modified ELM algorithm.
Vorheriger Artikel The extreme learning machine learning algorithm with tunable activation function
Nächster Artikel Symmetric extreme learning machine

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Metadaten
Titel
A modified extreme learning machine with sigmoidal activation functions
verfasst von
Zhixiang X. Chen
Houying Y. Zhu
Yuguang G. Wang
Publikationsdatum
01.03.2013
Verlag
Springer-Verlag
Erschienen in
Neural Computing and Applications / Ausgabe 3-4/2013
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-012-0860-2

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