ν-Arc: Ensemble learning in the presence of outliers

G. Rätsch, B. Schölkopf, A. Smola, Klaus Muller, T. Onoda, S. Mika

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

AdaBoost and other ensemble methods have successfully been applied to a number of classification tasks' seemingly defying problems of overfitting. AdaBoost performs gradient descent in an error function with respect to the margin' asymptotically concentrating on the patterns which are hardest to learn. For very noisy problems' however' this can be disadvantageous. Indeed' theoretical analysis has shown that the margin distribution' as opposed to just the minimal margin' plays a crucial role in understanding this phenomenon. Loosely speaking' some outliers should be tolerated if this has the benefit of substantially the margin on the remaining points. We propose a new boosting algorithm which allows for the possibility of a pre-specified fraction of points to lie in the margin area or even on the wrong side of the decision boundary.

Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems
PublisherNeural information processing systems foundation
Pages561-567
Number of pages7
ISBN (Print)0262194503, 9780262194501
Publication statusPublished - 2000 Jan 1
Externally publishedYes
Event13th Annual Neural Information Processing Systems Conference, NIPS 1999 - Denver, CO, United States
Duration: 1999 Nov 291999 Dec 4

Other

Other13th Annual Neural Information Processing Systems Conference, NIPS 1999
CountryUnited States
CityDenver, CO
Period99/11/2999/12/4

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ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

Cite this

Rätsch, G., Schölkopf, B., Smola, A., Muller, K., Onoda, T., & Mika, S. (2000). ν-Arc: Ensemble learning in the presence of outliers. In Advances in Neural Information Processing Systems (pp. 561-567). Neural information processing systems foundation.