Opening book details…
Can I read Multi-Label Classification with Optimal Thresholding for Multi-Composition Spectroscopic Analysis on EtoBox?
Multi-Label Classification with Optimal Thresholding for Multi-Composition Spectroscopic Analysis by Gan, Luyun; Yuen, Brosnan; Lu, Tao is a Computer Science article available to read on EtoBox.
What is Multi-Label Classification with Optimal Thresholding for Multi-Composition Spectroscopic Analysis about?
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.
Who reads Multi-Label Classification with Optimal Thresholding for Multi-Composition Spectroscopic Analysis?
It is typically read by researchers, students, and practitioners in Computer Science.
- Author
- Gan, Luyun; Yuen, Brosnan; Lu, Tao
- Publisher
- Multidisciplinary Digital Publishing Institute (MDPI) (ISSN 2504-4990)
- Published
- 2019
- Language
- EN
- Field
- Computer Science (Physical Sciences)