title: Multicategory angle-based large-margin classification.
abstract: Large-margin classifiers are popular methods for classification. Among existing simultaneous multicategory large-margin classifiers, a common approach is to learnkdifferent functions for ak-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.
authors.affiliation: Department of Statistics and Operations Research, University of North Carolina, Chapel Hill, North Carolina 27599, U.S.A.
authors.forename: Chong
authors.initials: C
authors.lastname: Zhang
authors.affiliation: Department of Statistics and Operations Research, University of North Carolina, Chapel Hill, North Carolina 27599, U.S.A.
authors.forename: Yufeng
authors.initials: Y
authors.lastname: Liu
keywords: Hard classification; Probability estimation; Soft classification; Support vector machine