Sammelbandbeitrag - Wissenschaftlicher Artikel | 2015 | Peer reviewed

SVM Ensembles Are Better When Different Kernel Types Are Combined

Autor*innen

Stork, Jörg ; Ramos, Ricardo ; Koch, Patrick ; Konen, Wolfgang

Sammel-/ Konferenzband

Data Science, Learning By Latent Structures, and Knowledge Discovery

ISBN-10

366244982

URL

http://www.springer.com/de/book/9783662449820

Weitere Details

Sprache

englisch

Darstellungsform

Text

Seitenbereich

191 - 201

Zitierstile

Stork, J., Ramos, R., Koch, P., & Konen, W. (2015). SVM Ensembles Are Better When Different Kernel Types Are Combined. In B. Lausen, S. Krolak-Schwerdt, & M. Böhmer, Data Science, Learning By Latent Structures, and Knowledge Discovery (pp. 191–201). Heidelberg: Springer. Retrieved from http://www.springer.com/de/book/9783662449820

In die Zwischenablage kopieren

Stork, J., Ramos, R., Koch, P. and Konen, W., 2015. SVM Ensembles Are Better When Different Kernel Types Are Combined. In: Data Science, Learning By Latent Structures, and Knowledge Discovery. [online] Heidelberg: Springer, pp.191–201. Available at: <http://www.springer.com/de/book/9783662449820>.

In die Zwischenablage kopieren

Stork, Jörg u. a. 2015. SVM Ensembles Are Better When Different Kernel Types Are Combined. In Data Science, Learning By Latent Structures, and Knowledge Discovery. Heidelberg: Springer, 191–201. http://www.springer.com/de/book/9783662449820.

American Psychological Association 6th edition (DOI)

Anglia Ruskin University - Harvard

Gesellschaft für Bildung und Forschung in Europa - Harvard