his.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • 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
Empirical Study of Time Efficiency and Accuracy of Support Vector Machines Using an Improved Version of PSVM
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. Department of Information Technology, University of Borås, Borås, Sweden. (Skövde Artificial Intelligence Lab (SAIL))
Department of Information Technology, University of Borås, Borås, Sweden.
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. (Skövde Artificial Intelligence Lab (SAIL))
2015 (English)In: Proceedings of the 2015 International Conference on Parallel and Distributed Processing Techniques and Applications: PDPTA 2015: Volume 1 / [ed] Hamid R. Arabnia, Hiroshi Ishii, Kazuki Joe, Hiroaki Nishikawa, Havaru Shouno, Printed in the United States of America: CSREA Press, 2015, Vol. 1, 177-183 p.Conference paper (Refereed)
Abstract [en]

We present a significantly improved implementation of a parallel SVM algorithm (PSVM) together with a comprehensive experimental study. Support Vector Machines (SVM) is one of the most well-known machine learning classification techniques. PSVM employs the Interior Point Method, which is a solver used for SVM problems that has a high potential of parallelism. We improve PSVM regarding its structure and memory management for contemporary processor architectures. We perform a number of experiments and study the impact of the reduced column size p and other important parameters as C and gamma on the class-prediction accuracy and training time. The experimental results show that there exists a threshold between the number of computational cores and the training time, and that choosing an appropriate value of p effects the choice of the C and gamma parameters as well as the accuracy.

Place, publisher, year, edition, pages
Printed in the United States of America: CSREA Press, 2015. Vol. 1, 177-183 p.
Keyword [en]
parallel svm, processor technology, training time
National Category
Computer Science
Research subject
Technology
Identifiers
URN: urn:nbn:se:his:diva-11644ISBN: 1-60132-400-6 ISBN: 1-60132-401- 4 ISBN: 1 -60132-402-2 OAI: oai:DiVA.org:his-11644DiVA: diva2:866010
Conference
PDPTA'15 - The 21st International Conference on Parallel and Distributed Processing Techniques and Applications, Las Vegas, July 27-30, 2015
Available from: 2015-10-30 Created: 2015-10-30 Last updated: 2016-02-10Bibliographically approved

Open Access in DiVA

No full text

Other links

Länk till fulltext

Search in DiVA

By author/editor
Tavara, ShirinDahlbom, Anders
By organisation
School of InformaticsThe Informatics Research Centre
Computer Science

Search outside of DiVA

GoogleGoogle Scholar

Total: 445 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • 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