Jörg Stork, Ricardo Ramos, Patrick Koch, Wolfgang Konen. SVM Ensembles Are Better When Different Kernel Types Are Combined. In Berthold Lausen, Sabine Krolak-Schwerdt, Matthias Böhmer 0002, editors, Data Science, Learning by Latent Structures, and Knowledge Discovery [revised versions of selected papers presented during the European Conference on Data Analysis (ECDA 2013), Luxembourg, July 2013]. Studies in Classification, Data Analysis, and Knowledge Organization, pages 191-201, Springer, 2013. [doi]
@inproceedings{StorkRKK13,
title = {SVM Ensembles Are Better When Different Kernel Types Are Combined},
author = {Jörg Stork and Ricardo Ramos and Patrick Koch and Wolfgang Konen},
year = {2013},
doi = {10.1007/978-3-662-44983-7_17},
url = {http://dx.doi.org/10.1007/978-3-662-44983-7_17},
researchr = {https://researchr.org/publication/StorkRKK13},
cites = {0},
citedby = {0},
pages = {191-201},
booktitle = {Data Science, Learning by Latent Structures, and Knowledge Discovery [revised versions of selected papers presented during the European Conference on Data Analysis (ECDA 2013), Luxembourg, July 2013]},
editor = {Berthold Lausen and Sabine Krolak-Schwerdt and Matthias Böhmer 0002},
series = {Studies in Classification, Data Analysis, and Knowledge Organization},
publisher = {Springer},
isbn = {978-3-662-44982-0},
}