Reinforced Angle-based Multicategory Support Vector Machines.

TitleReinforced Angle-based Multicategory Support Vector Machines.
Publication TypeJournal Article
Year of Publication2016
AuthorsZhang, Chong, Yufeng Liu, Junhui Wang, and Hongtu Zhu
JournalJ Comput Graph Stat
Date Published2016

The Support Vector Machine (SVM) is a very popular classification tool with many successful applications. It was originally designed for binary problems with desirable theoretical properties. Although there exist various Multicategory SVM (MSVM) extensions in the literature, some challenges remain. In particular, most existing MSVMs make use of classification functions for a -class problem, and the corresponding optimization problems are typically handled by existing quadratic programming solvers. In this paper, we propose a new group of MSVMs, namely the Reinforced Angle-based MSVMs (RAMSVMs), using an angle-based prediction rule with - 1 functions directly. We prove that RAMSVMs can enjoy Fisher consistency. Moreover, we show that the RAMSVM can be implemented using the very efficient coordinate descent algorithm on its dual problem. Numerical experiments demonstrate that our method is highly competitive in terms of computational speed, as well as classification prediction performance. Supplemental materials for the article are available online.

Alternate JournalJ Comput Graph Stat
Original PublicationReinforced angle-based multicategory support vector machines.
PubMed ID27891045
PubMed Central IDPMC5120762
Grant ListP01 CA142538 / CA / NCI NIH HHS / United States
R01 CA149569 / CA / NCI NIH HHS / United States
R01 MH086633 / MH / NIMH NIH HHS / United States