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.
| Original language | English |
|---|---|
| Title of host publication | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Publisher | Springer Verlag |
| Pages | 341-344 |
| Number of pages | 4 |
| Volume | 1805 |
| ISBN (Print) | 3540673822, 9783540673828 |
| Publication status | Published - 2000 |
| Externally published | Yes |
| Event | 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2000 - Kyoto, Japan Duration: 2000 Apr 18 → 2000 Apr 20 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 1805 |
| ISSN (Print) | 03029743 |
| ISSN (Electronic) | 16113349 |
Other
| Other | 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2000 |
|---|---|
| Country | Japan |
| City | Kyoto |
| Period | 00/4/18 → 00/4/20 |
Fingerprint
ASJC Scopus subject areas
- Computer Science(all)
- Theoretical Computer Science
Cite this
Robust ensemble learning for data mining. / Rätsch, Gunnar; Schölkopf, Bernhard; Smola, Alexander Johannes; Mika, Sebastian; Onoda, Takashi; Muller, Klaus.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 1805 Springer Verlag, 2000. p. 341-344 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1805).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
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 - Muller, Klaus
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
M3 - Conference contribution
AN - SCOPUS:84869096933
SN - 3540673822
SN - 9783540673828
VL - 1805
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 341
EP - 344
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PB - Springer Verlag
ER -