Bayesian learning using Gaussian process for gas identification

Amine Bermak; Sofiane Brahim Belhouari

doi:10.1109/TIM.2006.873804

Bayesian learning using Gaussian process for gas identification

Amine Bermak^*, Sofiane Brahim Belhouari

^*Corresponding author for this work

Hong Kong University of Science and Technology

Research output: Contribution to journal › Article › peer-review

36 Citations (Scopus)

Abstract

In this paper, a novel gas identification approach based on Gaussian process (GP) combined with principal components analysis is proposed. The effectiveness of this approach has been successfully demonstrated on an experimentally obtained dataset. Our aim is the identification of different gases with an array of commercial Taguchi gas sensors (TGS) as well as microelectronic gas sensors. The proposed approach is shown to outperform both K nearest neighbor (KNN) and multilayer perceptron (MLP) classifiers.

Original language	English
Pages (from-to)	787-792
Number of pages	6
Journal	IEEE Transactions on Instrumentation and Measurement
Volume	55
Issue number	3
DOIs	https://doi.org/10.1109/TIM.2006.873804
Publication status	Published - Jun 2006
Externally published	Yes

Keywords

Bayesian learning
Gas identification
Gas sensor array
Gaussian processes (GPs)
Pattern recognition

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10.1109/TIM.2006.873804

Cite this

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abstract = "In this paper, a novel gas identification approach based on Gaussian process (GP) combined with principal components analysis is proposed. The effectiveness of this approach has been successfully demonstrated on an experimentally obtained dataset. Our aim is the identification of different gases with an array of commercial Taguchi gas sensors (TGS) as well as microelectronic gas sensors. The proposed approach is shown to outperform both K nearest neighbor (KNN) and multilayer perceptron (MLP) classifiers.",

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author = "Amine Bermak and Belhouari, {Sofiane Brahim}",

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KW - Bayesian learning

KW - Gas identification

KW - Gas sensor array

KW - Gaussian processes (GPs)

KW - Pattern recognition

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Bayesian learning using Gaussian process for gas identification

Abstract

Keywords

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