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Gas identification using density models
- Source :
-
Pattern Recognition Letters . May2005, Vol. 26 Issue 6, p699-706. 8p. - Publication Year :
- 2005
-
Abstract
- Abstract: In this paper we 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 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 Gaussian mixture models. [Copyright &y& Elsevier]
- Subjects :
- *PATTERN perception
*DETECTORS
*FORM perception
*SENSORY perception
Subjects
Details
- Language :
- English
- ISSN :
- 01678655
- Volume :
- 26
- Issue :
- 6
- Database :
- Academic Search Index
- Journal :
- Pattern Recognition Letters
- Publication Type :
- Academic Journal
- Accession number :
- 17637614
- Full Text :
- https://doi.org/10.1016/j.patrec.2004.09.020