Title | Reinforced Angle-based Multicategory Support Vector Machines. |
Publication Type | Journal Article |
Year of Publication | 2016 |
Authors | Zhang, Chong, Yufeng Liu, Junhui Wang, and Hongtu Zhu |
Journal | J Comput Graph Stat |
Volume | 25 |
Issue | 3 |
Pagination | 806-825 |
Date Published | 2016 |
ISSN | 1061-8600 |
Abstract | 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. |
DOI | 10.1080/10618600.2015.1043010 |
Alternate Journal | J Comput Graph Stat |
Original Publication | Reinforced angle-based multicategory support vector machines. |
PubMed ID | 27891045 |
PubMed Central ID | PMC5120762 |
Grant List | P01 CA142538 / CA / NCI NIH HHS / United States R01 CA149569 / CA / NCI NIH HHS / United States R01 MH086633 / MH / NIMH NIH HHS / United States |