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- W2102852743 abstract "In this paper, a classification framework is developed to address the issue that empirical determination of the parameters and their values typically makes a classification framework less adaptive and general to different data sets and application domains. Experimental results show that our proposed framework achieves (1) better performance over other comparative supervised classification methods, (2) more robust to imbalanced data sets, and (3) smaller performance variance to different data sets." @default.
- W2102852743 created "2016-06-24" @default.
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- W2102852743 date "2009-08-01" @default.
- W2102852743 modified "2023-10-18" @default.
- W2102852743 title "Supervised multi-class classification with adaptive and automatic parameter tuning" @default.
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- W2102852743 doi "https://doi.org/10.1109/iri.2009.5211595" @default.
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