Multi-Label Classification with Optimal Thresholding for Multi-Composition Spectroscopic Analysis
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In this paper, we implement multi-label neural networks with optimal thresholding to identify gas species among a multiple gas mixture in a cluttered environment. Using infrared absorption spectroscopy and tested on synthesized spectral datasets, our approach outperforms conventional binary relevance-partial least squares discriminant analysis when the signal-to-noise ratio and training sample size are sufficient.
2017 ◽
Vol 11
(3)
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pp. 539-549
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1978 ◽
Vol 24
(2)
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pp. 229-237
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1981 ◽
Vol 39
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pp. 32-33
1981 ◽
Vol 39
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pp. 226-227
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1989 ◽
Vol 47
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pp. 84-85
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1979 ◽
Vol 10
(4)
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pp. 221-230
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2020 ◽
Vol 63
(11)
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pp. 3855-3864
2020 ◽
Vol 63
(1)
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pp. 345-356
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