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Stacked Sparse Auto-Encoders (SSAE) Based Electronic Nose for Chinese Liquors Classification
This paper presents a stacked sparse auto-encoder (SSAE) based deep learning method for an electronic nose (e-nose) system to classify different brands of Chinese liquors. It is well known that preprocessing; feature extraction (generation and reduction) are necessary steps in traditional data-proce...
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| Publicado no: | Sensors (Basel) |
|---|---|
| Main Authors: | , , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
MDPI
2017
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC5751720/ https://ncbi.nlm.nih.gov/pubmed/29292772 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.3390/s17122855 |
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