Fast Extended One-Versus-Rest Multi-Label Support Vector Machine Using Approximate Extreme Points

Fast Extended One-Versus-Rest Multi-Label Support Vector Machine Using Approximate Extreme Points

Existing extended one-versus-rest multi-label support vector machine (OVR-ESVM) adopting non-linear kernel is seriously restricted by excessive training time when it is applied to large-scale data set. In order to overcome this problem, we improve the OVR-ESVM by introducing the principle of approxi...

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Bibliographic Details
Main Authors: Zhongwei Sun, Zhongwen Guo, Chao Liu, Xupeng Wang, Jing Liu, Shiyong Liu
Format: Article
Language:English
Published: IEEE 2017-01-01
Series:IEEE Access
Subjects:
Support vector machine
multi-label classification
approximate extreme points
label correlation
non-linear kernel
Online Access:https://ieeexplore.ieee.org/document/7914677/

Internet

https://ieeexplore.ieee.org/document/7914677/

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