Cargando…
A Novel Semi-Supervised Electronic Nose Learning Technique: M-Training
When an electronic nose (E-nose) is used to distinguish different kinds of gases, the label information of the target gas could be lost due to some fault of the operators or some other reason, although this is not expected. Another fact is that the cost of getting the labeled samples is usually high...
Autores principales: | Jia, Pengfei, Huang, Tailai, Duan, Shukai, Ge, Lingpu, Yan, Jia, Wang, Lidan |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2016
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4813945/ https://www.ncbi.nlm.nih.gov/pubmed/26985898 http://dx.doi.org/10.3390/s16030370 |
Ejemplares similares
-
A Novel Semi-Supervised Method of Electronic Nose for Indoor Pollution Detection Trained by M-S4VMs
por: Huang, Tailai, et al.
Publicado: (2016) -
A Novel Pre-Processing Technique for Original Feature Matrix of Electronic Nose Based on Supervised Locality Preserving Projections
por: Jia, Pengfei, et al.
Publicado: (2016) -
A Novel Optimization Technique to Improve Gas Recognition by Electronic Noses Based on the Enhanced Krill Herd Algorithm
por: Wang, Li, et al.
Publicado: (2016) -
Enhancing Electronic Nose Performance Based on a Novel QPSO-KELM Model
por: Peng, Chao, et al.
Publicado: (2016) -
Electronic Nose Feature Extraction Methods: A Review
por: Yan, Jia, et al.
Publicado: (2015)