@inproceedings{dc1d407fee70404984229b06b500f5e1,
title = "A comparative study of density models for gas identification using microelectronic gas sensor",
abstract = "The aim of this paper is to compare the accuracy of a range of advanced density models for gas identification from sensor array signals. Density estimation is applied in the construction of classifiers through the use of Bayes rule. Experiments on real sensors' data has proved the effectiveness of the approach with an excellent classification performance. We compare the classification accuracy of four density models, Gaussian mixture models, generative topographic mapping, probabilistic PCA mixture and k nearest neighbors. On our gas sensors data, the best performance was achieved by the Gaussian mixture models.",
keywords = "Brain modeling, Gas detectors, Linear discriminant analysis, Microelectronics, Nearest neighbor searches, Pattern recognition, Principal component analysis, Sensor arrays, Signal processing, Thin film sensors",
author = "S. Brahim-Belhouari and A. Bermak and Guangfen Wei and Chan, {P. C.H.}",
note = "Publisher Copyright: {\textcopyright} 2003 IEEE.; 3rd IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2003 ; Conference date: 14-12-2003 Through 17-12-2003",
year = "2003",
doi = "10.1109/ISSPIT.2003.1341079",
language = "English",
series = "Proceedings of the 3rd IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2003",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "138--141",
booktitle = "Proceedings of the 3rd IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2003",
address = "United States",
}