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(Created page with "== Presented by == Yan Yu Chen, Qisi Deng, Hengxin Li, Bochao Zhang == Introduction == == Previous Work == == Motivation == == Model Architecture == == ILSVRC 20...")
 
(Introduction)
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Yan Yu Chen, Qisi Deng, Hengxin Li, Bochao Zhang
 
Yan Yu Chen, Qisi Deng, Hengxin Li, Bochao Zhang
  
== Introduction ==  
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== Introduction ==
 +
In the past two decades, due to their surprising classi- fication capability, support vector machine (SVM) [1] and its variants [2]–[4] have been extensively used in classification applications.
 +
Least square support vector machine (LS-SVM) and proximal sup- port vector machine (PSVM) have been widely used in binary classification applications. The conventional LS-SVM and PSVM cannot be used in regression and multiclass classification appli- cations directly, although variants of LS-SVM and PSVM have been proposed to handle such cases.
  
 
== Previous Work ==  
 
== Previous Work ==  

Revision as of 23:26, 8 November 2018

Presented by

Yan Yu Chen, Qisi Deng, Hengxin Li, Bochao Zhang

Introduction

In the past two decades, due to their surprising classi- fication capability, support vector machine (SVM) [1] and its variants [2]–[4] have been extensively used in classification applications. Least square support vector machine (LS-SVM) and proximal sup- port vector machine (PSVM) have been widely used in binary classification applications. The conventional LS-SVM and PSVM cannot be used in regression and multiclass classification appli- cations directly, although variants of LS-SVM and PSVM have been proposed to handle such cases.

Previous Work

Motivation

Model Architecture

ILSVRC 2014 Challenge Results

Conclusion

Critiques

References