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 |
ASJC Scopus subject areas
- Computer Science(all)
- Theoretical Computer Science
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Cite this
Rätsch, G., Schölkopf, B., Smola, A. J., Mika, S., Onoda, T., & Muller, K. (2000). Robust ensemble learning for data mining. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1805, pp. 341-344). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1805). Springer Verlag.