Uncertainty-safe large scale support vector machines

2017 ◽  
Vol 109 ◽  
pp. 215-230 ◽  
Author(s):  
Nicolas Couellan ◽  
Wenjuan Wang
Keyword(s):  
Support Vector Machines ◽  
Large Scale ◽  
Support Vector ◽  
Vector Machines

Large-scale pinball twin support vector machines

2021 ◽  
Author(s):  
M. Tanveer ◽  
A. Tiwari ◽  
R. Choudhary ◽  
M. A. Ganaie
Keyword(s):  
Support Vector Machines ◽  
Large Scale ◽  
Support Vector ◽  
Twin Support Vector Machines ◽  
Vector Machines

scholarly journals Large-scale ligand-based predictive modelling using support vector machines

2016 ◽  
Vol 8 (1) ◽  
Author(s):  
Jonathan Alvarsson ◽  
Samuel Lampa ◽  
Wesley Schaal ◽  
Claes Andersson ◽  
Jarl E. S. Wikberg ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Large Scale ◽  
Predictive Modelling ◽  
Support Vector ◽  
Vector Machines

Large-scale robust transductive support vector machines

2017 ◽  
Vol 235 ◽  
pp. 199-209 ◽  
Author(s):  
Hakan Cevikalp ◽  
Vojtech Franc
Keyword(s):  
Support Vector Machines ◽  
Large Scale ◽  
Support Vector ◽  
Vector Machines

scholarly journals Selection of Support Vector Candidates Using Relative Support Distance for Sustainability in Large-Scale Support Vector Machines

2020 ◽  
Vol 10 (19) ◽  
pp. 6979
Author(s):  
Minho Ryu ◽  
Kichun Lee
Keyword(s):  
Support Vector Machines ◽  
Quadratic Programming ◽  
Decision Trees ◽  
Programming Problem ◽  
Large Scale ◽  
Classification Performance ◽  
Quadratic Programming Problem ◽  
Support Vector ◽  
Training Time ◽  
Vector Machines

Support vector machines (SVMs) are a well-known classifier due to their superior classification performance. They are defined by a hyperplane, which separates two classes with the largest margin. In the computation of the hyperplane, however, it is necessary to solve a quadratic programming problem. The storage cost of a quadratic programming problem grows with the square of the number of training sample points, and the time complexity is proportional to the cube of the number in general. Thus, it is worth studying how to reduce the training time of SVMs without compromising the performance to prepare for sustainability in large-scale SVM problems. In this paper, we proposed a novel data reduction method for reducing the training time by combining decision trees and relative support distance. We applied a new concept, relative support distance, to select good support vector candidates in each partition generated by the decision trees. The selected support vector candidates improved the training speed for large-scale SVM problems. In experiments, we demonstrated that our approach significantly reduced the training time while maintaining good classification performance in comparison with existing approaches.

Sign in / Sign up

Export Citation Format

Share Document