Purity Filtering: An Instance Selection Method for Support Vector Machines

2019 ◽  
pp. 21-35
Author(s):  
David Morán-Pomés ◽  
Lluís A. Belanche-Muñoz
Keyword(s):  
Support Vector Machines ◽  
Selection Method ◽  
Support Vector ◽  
Instance Selection ◽  
Vector Machines

An Evolutionary Multiobjective Model and Instance Selection for Support Vector Machines With Pareto-Based Ensembles

2017 ◽  
Vol 21 (6) ◽  
pp. 863-877 ◽  
Author(s):  
Alejandro Rosales-Perez ◽  
Salvador Garcia ◽  
Jesus A. Gonzalez ◽  
Carlos A. Coello Coello ◽  
Francisco Herrera
Keyword(s):  
Support Vector Machines ◽  
Support Vector ◽  
Instance Selection ◽  
Vector Machines ◽  
Selection For

Parameters Selection and Application of LS-SVM Based on Chaotic Ant Swarm Algorithm

2011 ◽  
Vol 204-210 ◽  
pp. 423-426
Author(s):  
Chun Li Xie ◽  
Dan Dan Zhao ◽  
Juan Wang ◽  
Cheng Shao
Keyword(s):  
Support Vector Machines ◽  
Nonlinear Systems ◽  
Fitness Function ◽  
Selection Method ◽  
Support Vector ◽  
Mean Square ◽  
Parameters Selection ◽  
Vector Machines ◽  
The Mean ◽  
Simulation Results

Parameters selection plays an important role for the performance of least squares support vector machines (LS-SVM). In this paper, a novel parameters selection method for LS-SVM is presented based on chaotic ant swarm (CAS) algorithm. Using this method, the optimization model is established, within which the fitness function is the mean square error (MSE) index, and the constraints are the ranges of the designing parameters. The proposed method is used in the identification for inverse model of the nonlinear systems, and simulation results are given to show the efficiency.

Fast instance selection for speeding up support vector machines

2013 ◽  
Vol 45 ◽  
pp. 1-7 ◽  
Author(s):  
Jingnian Chen ◽  
Caiming Zhang ◽  
Xiaoping Xue ◽  
Cheng-Lin Liu
Keyword(s):  
Support Vector Machines ◽  
Support Vector ◽  
Instance Selection ◽  
Vector Machines ◽  
Selection For
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