Valley-loss regular simplex support vector machine for robust multiclass classification

2021 ◽  
Vol 216 ◽  
pp. 106801
Author(s):  
Long Tang ◽  
Yingjie Tian ◽  
Wenjun Li ◽  
Panos M. Pardalos
Keyword(s):  
Support Vector Machine ◽  
Multiclass Classification ◽  
Support Vector ◽  
Regular Simplex

Structural improved regular simplex support vector machine for multiclass classification

2020 ◽  
Vol 91 ◽  
pp. 106235 ◽  
Author(s):  
Long Tang ◽  
Yingjie Tian ◽  
Wenjun Li ◽  
Panos M. Pardalos
Keyword(s):  
Support Vector Machine ◽  
Multiclass Classification ◽  
Support Vector ◽  
Regular Simplex

SUPPORT VECTOR MACHINE NETWORKS FOR MULTI-CLASS CLASSIFICATION

2005 ◽  
Vol 19 (06) ◽  
pp. 775-786 ◽  
Author(s):  
FRANK Y. SHIH ◽  
KAI ZHANG
Keyword(s):  
Neural Networks ◽  
Support Vector Machine ◽  
Pattern Classification ◽  
Multiclass Classification ◽  
Support Vector ◽  
Competitive Performance ◽  
Multi Class Classification ◽  
New Framework

The support vector machine (SVM) has recently attracted growing interest in pattern classification due to its competitive performance. It was originally designed for two-class classification, and many researchers have been working on extensions to multiclass. In this paper, we present a new framework that adapts the SVM with neural networks and analyze the source of misclassification in guiding our preprocessing for optimization in multiclass classification. We perform experiments on the ORL database and the results show that our framework can achieve high recognition rates.

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