Title | Multicategory angle-based large-margin classification. |
Publication Type | Journal Article |
Year of Publication | 2014 |
Authors | Zhang, Chong, and Yufeng Liu |
Journal | Biometrika |
Volume | 101 |
Issue | 3 |
Pagination | 625-640 |
Date Published | 2014 Sep |
ISSN | 0006-3444 |
Abstract | Large-margin classifiers are popular methods for classification. Among existing simultaneous multicategory large-margin classifiers, a common approach is to learn different functions for a -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. |
DOI | 10.1093/biomet/asu017 |
Alternate Journal | Biometrika |
Original Publication | Multicategory angle-based large-margin classification. |
PubMed ID | 26538663 |
PubMed Central ID | PMC4629508 |
Grant List | P01 CA142538 / CA / NCI NIH HHS / United States |
Project: