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A support vector method for multivariate performance measures
Abstract
This paper presents a Support Vector Method for optimizing multivariate nonlinear performance measures like the F1-score. Taking a multivariate prediction approach, we give an algorithm with which such multivariate SVMs can be trained in polynomial time for large classes of potentially non-linear performance measures, in particular ROCArea and all measures that can be computed from the contingency table. The conventional classification SVM arises as a special case of our method.
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Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
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Metadata
- Authors
- Joachims, T.
- Venue
- Proceedings of the 22nd international conference on Machine learning - ICML '05
- Publisher
- ACM Press
- Published
- 2005
- Pages
- 377-384
- OpenAlex
- W2070771761
- Cited by
- 786
- References
- 1
- FWCI
- 19.32
- Type
- conference paper
- Language
- en
- Open access
- closed
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