TY - GEN
T1 - Robust ensemble learning for data mining
AU - Rätsch, Gunnar
AU - Schölkopf, Bernhard
AU - Smola, Alexander Johannes
AU - Mika, Sebastian
AU - Onoda, Takashi
AU - Müller, Klaus Robert
PY - 2000
Y1 - 2000
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84869096933&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84869096933&partnerID=8YFLogxK
U2 - 10.1007/3-540-45571-x_39
DO - 10.1007/3-540-45571-x_39
M3 - Conference contribution
AN - SCOPUS:84869096933
SN - 3540673822
SN - 9783540673828
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 341
EP - 344
BT - Knowledge Discovery and Data Mining
A2 - Terano, Takao
A2 - Liu, Huan
A2 - Chen, Arbee L.P.
PB - Springer Verlag
T2 - 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2000
Y2 - 18 April 2000 through 20 April 2000
ER -