Ändra sökning
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Improving the Quality of User Generated Data Sets for Activity Recognition
School of Computing and Mathematics, Ulster University.
School of Computing and Mathematics, Ulster University .
Dipartimento dell’ingegneria dell’informazione, Universita Politecnica Delle Marche, Ancona, .
School of Computing and Mathematics, Ulster University.
Visa övriga samt affilieringar
Antal upphovsmän: 122016 (Engelska)Ingår i: Ubiquitous Computing and Ambient Intelligence: 10th International Conference, UCAmI 2016, San Bartolomé de Tirajana, Gran Canaria, Spain, November 29 – December 2, 2016, Part II / [ed] Carmelo R. García, Pino Caballero-Gil, Mike Burmester, Alexis Quesada-Arencibia, Springer, 2016, Vol. 2, s. 104-110Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

It is fully appreciated that progress in the development of data driven approaches to activity recognition are being hampered due to the lack of large scale, high quality, annotated data sets. In an effort to address this the Open Data Initiative (ODI) was conceived as a potential solution for the creation of shared resources for the collection and sharing of open data sets. As part of this process, an analysis was undertaken of datasets collected using a smart environment simulation tool. A noticeable difference was found in the first 1–2 cycles of users generating data. Further analysis demonstrated the effects that this had on the development of activity recognition models with a decrease of performance for both support vector machine and decision tree based classifiers. The outcome of the study has led to the production of a strategy to ensure an initial training phase is considered prior to full scale collection of the data.

Ort, förlag, år, upplaga, sidor
Springer, 2016. Vol. 2, s. 104-110
Serie
Lecture Notes in Computer Science, ISSN 0302-9743 ; 10070
Nationell ämneskategori
Medieteknik
Forskningsämne
Distribuerade datorsystem
Identifikatorer
URN: urn:nbn:se:ltu:diva-60647DOI: 10.1007/978-3-319-48799-1_13ISI: 000389507400013Scopus ID: 2-s2.0-85009788304ISBN: 978-3-319-48798-4 (tryckt)ISBN: 978-3-319-48799-1 (tryckt)OAI: oai:DiVA.org:ltu-60647DiVA, id: diva2:1049109
Konferens
10th International Conference, UCAmI 2016, San Bartolomé de Tirajana, Gran Canaria, Spain, November 29 – December 2, 2016
Tillgänglig från: 2016-11-23 Skapad: 2016-11-23 Senast uppdaterad: 2018-07-10Bibliografiskt granskad

Open Access i DiVA

Fulltext saknas i DiVA

Övriga länkar

Förlagets fulltextScopus

Personposter BETA

Synnes, KåreHallberg, Josef

Sök vidare i DiVA

Av författaren/redaktören
Synnes, KåreHallberg, Josef
Av organisationen
Datavetenskap
Medieteknik

Sök vidare utanför DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetricpoäng

doi
isbn
urn-nbn
Totalt: 299 träffar
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf