@inproceedings{3fb5b031c1dc4d91b6d506d581b5f2db,
title = "Robust ensemble learning for data mining",
abstract = "We propose a new boosting algorithm which similaxly to v-Support-Vector Classification allows for the possibility of a pre-specified fraction v of points to lie in the margin area or even on the wrong side of the decision boundary. It gives a nicely interpretable way of controlling the trade-off between minimizing training error and capacity. Furthermore, it can act as a filter for finding and selecting informative patterns from a database.",
author = "Gunnar R{\"a}tsch and Bernhard Sch{\"o}lkopf and Smola, {Alexander Johannes} and Sebastian Mika and Takashi Onoda and M{\"u}ller, {Klaus Robert}",
note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2000.; 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2000 ; Conference date: 18-04-2000 Through 20-04-2000",
year = "2000",
doi = "10.1007/3-540-45571-x_39",
language = "English",
isbn = "3540673822",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "341--344",
editor = "Takao Terano and Huan Liu and Chen, {Arbee L.P.}",
booktitle = "Knowledge Discovery and Data Mining",
}