Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Unsupervised feature learning for electronic nose data applied to Bacteria Identification in Blood.
Örebro University, School of Science and Technology. (AASS)ORCID iD: 0000-0002-0579-7181
Örebro University, School of Science and Technology. (AASS)ORCID iD: 0000-0002-3122-693X
2011 (English)Conference paper, Poster (with or without abstract) (Refereed)
Abstract [en]

Electronic nose (e-nose) data represents multivariate time-series from an array of chemical gas sensors exposed to a gas. This data is a new data set for usewith deep learning methods, and is highly suitable since e-nose data is complexand difficult to interpret for human experts. Furthermore, this data set presentsa number of interesting challenges for deep learning architectures per se. In this work we present a first study of e-nose data classification using deep learningwhen testing for the presence of bacteria in blood and agar solutions. We showin this study that deep learning outperforms hand-selected strategy based methods which has been previously tried with the same data set.

Place, publisher, year, edition, pages
2011.
National Category
Engineering and Technology
Research subject
Computer and Systems Science
Identifiers
URN: urn:nbn:se:oru:diva-24197OAI: oai:DiVA.org:oru-24197DiVA: diva2:542614
Conference
NIPS 2011 Workshop on Deep Learning and Unsupervised Feature Learning
Available from: 2012-08-06 Created: 2012-08-02 Last updated: 2017-10-17Bibliographically approved
In thesis
1. Modeling time-series with deep networks
Open this publication in new window or tab >>Modeling time-series with deep networks
2014 (English)Doctoral thesis, comprehensive summary (Other academic)
Place, publisher, year, edition, pages
Örebro: Örebro university, 2014. 56 p.
Series
Örebro Studies in Technology, ISSN 1650-8580 ; 63
Keyword
multivariate time-series, deep learning, representation learning, unsupervised
National Category
Computer and Information Science
Research subject
Information technology
Identifiers
urn:nbn:se:oru:diva-39415 (URN)978-91-7529-054-6 (ISBN)
Public defence
2015-02-02, Hörsalen, Musikhögskolan, Örebro universitet, Fakultetsgatan 1, Örebro, 13:15 (English)
Opponent
Supervisors
Available from: 2014-12-08 Created: 2014-12-08 Last updated: 2017-10-17Bibliographically approved

Open Access in DiVA

fulltext(216 kB)427 downloads
File information
File name FULLTEXT01.pdfFile size 216 kBChecksum SHA-512
ce290f8b2419019e5bf6fcf196311665085986b672ddde32ea10efeeba966ee30f0676b54c7ae8b4332733919deb8982f38c83f0de117a04e7f2d145cbe571e4
Type fulltextMimetype application/pdf

Other links

http://deeplearningworkshopnips2011.wordpress.com/contributions/

Search in DiVA

By author/editor
Längkvist, MartinLoutfi, Amy
By organisation
School of Science and Technology
Engineering and Technology

Search outside of DiVA

GoogleGoogle Scholar
Total: 427 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 829 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf