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• W2162331139 abstract "Large-margin classifiers are popular methods for classification. Among existing simultaneous multicategory large-margin classifiers, a common approach is to learn k different functions for a k-class problem with a sum-to-zero constraint. Such a formulation can be inefficient. We propose a new multicategory angle-based large-margin classification framework. The proposed angle-based classifiers consider a simplex-based prediction rule without the sum-to-zero constraint, and enjoy more efficient computation. Many binary large-margin classifiers can be naturally generalized for multicategory problems through the angle-based framework. Theoretical and numerical studies demonstrate the usefulness of the angle-based methods." @default.
• W2162331139 created "2016-06-24" @default.
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• W2162331139 creator A5047835985 @default.
• W2162331139 date "2014-07-23" @default.
• W2162331139 modified "2023-10-16" @default.
• W2162331139 title "Multicategory angle-based large-margin classification" @default.
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• W2162331139 doi "https://doi.org/10.1093/biomet/asu017" @default.
• W2162331139 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/4629508" @default.
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