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Soft Computing

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01.09.2012 | Focus

Dynamic ensemble extreme learning machine based on sample entropy

verfasst von: Jun-hai Zhai, Hong-yu Xu, Xi-zhao Wang

Erschienen in: Soft Computing | Ausgabe 9/2012

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Abstract

Extreme learning machine (ELM) as a new learning algorithm has been proposed for single-hidden layer feed-forward neural networks, ELM can overcome many drawbacks in the traditional gradient-based learning algorithm such as local minimal, improper learning rate, and low learning speed by randomly selecting input weights and hidden layer bias. However, ELM suffers from instability and over-fitting, especially on large datasets. In this paper, a dynamic ensemble extreme learning machine based on sample entropy is proposed, which can alleviate to some extent the problems of instability and over-fitting, and increase the prediction accuracy. The experimental results show that the proposed approach is robust and efficient.

Vorheriger Artikel Evolutionary selection extreme learning machine optimization for regression

Nächster Artikel Generalized extreme learning machine acting on a metric space

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Titel: Dynamic ensemble extreme learning machine based on sample entropy
verfasst von: Jun-hai Zhai
Hong-yu Xu
Xi-zhao Wang
Publikationsdatum: 01.09.2012
Verlag: Springer-Verlag
Erschienen in: Soft Computing / Ausgabe 9/2012
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-012-0824-6

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