Training soft margin support vector machines by simulated annealing: A dual approach
作者：
Highlights：
• It was proposed a method to solve the dual quadratic optimization problem of SVMs.
• The proposal named SATE is based on simulated annealing.
• The objective function and constraints for SVM were successfully embedded in SATE.
• Our proposal is very simple to implement and achieved high sparseness.
• Our proposal was tested on real-world datasets and evaluated by statistical tests.
摘要
•It was proposed a method to solve the dual quadratic optimization problem of SVMs.•The proposal named SATE is based on simulated annealing.•The objective function and constraints for SVM were successfully embedded in SATE.•Our proposal is very simple to implement and achieved high sparseness.•Our proposal was tested on real-world datasets and evaluated by statistical tests.
论文关键词：Support vector machines,Simulated annealing,Learning methods
论文评审过程：Received 29 August 2016, Revised 6 June 2017, Accepted 11 June 2017, Available online 12 June 2017, Version of Record 20 June 2017.
论文官网地址：https://doi.org/10.1016/j.eswa.2017.06.016
