Adaptiveness and consistency of a class of online ensemble learning algorithms

Young, Carol; Yao, Ningshi; Zhang, Fumin

doi:10.1002/rnc.5292

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Adaptiveness and consistency of a class of online ensemble learning algorithms

2020
By Young, Carol ; Yao, Ningshi ; Zhang, Fumin
Source: International Journal of Robust and Nonlinear Control, 31(6), 2018-2043

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Journal Title:

International Journal of Robust and Nonlinear Control
Personal Author:

Young, Carol ; Yao, Ningshi ; Zhang, Fumin

Young, Carol ; Yao, Ningshi ; Zhang, Fumin Less -
NOAA Program & Office:

NOS (National Ocean Service)
Description:

SummaryExpert based ensemble learning algorithms often serve as online learning algorithms for an unknown, possibly time‐varying, probability distribution. Their simplicity allows flexibility in design choices, leading to variations that balance adaptiveness and consistency. This article provides an analytical framework to quantify the adaptiveness and consistency of expert based ensemble learning algorithms. With properly selected states, the algorithms are modeled as a Markov chains. Then quantitative metrics of adaptiveness and consistency can be calculated through mathematical formulas, other than relying on numerical simulations. Results are derived for several popular ensemble learning algorithms. Success of the method has also been demonstrated in both simulation and experimental results.
Keywords:

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Aerospace Engineering Biomedical Engineering Control And Systems Engineering Electrical And Electronic Engineering General Chemical Engineering Industrial And Manufacturing Engineering Mechanical Engineering
Source:

International Journal of Robust and Nonlinear Control, 31(6), 2018-2043
DOI:

https://doi.org/10.1002/rnc.5292
ISSN:

1049-8923;1099-1239;
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PDF
Publisher:

Wiley
Document Type:

Journal Article
Funding:

Grant no. NA16NOS0120028
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Accepted Manuscript
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urn:sha256:c49144030bc62a2dad3456fdd40ff40794508fa00b2f0aed0f6310b67316bd6d
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https://repository.library.noaa.gov/view/noaa/57086/noaa_57086_DS1.pdf
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Adaptiveness and consistency of a class of online ensemble learning algorithms

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