An efficient regularized K-nearest neighbor based weighted twin support vector regression
作者：
Highlights：
• Our RKNNWTSVR implements structural risk minimization principle by introducing extra regularization terms in each objective function.
• Our RKNNWTSVR cannot only help to alleviate overfitting issue and improve the generalization performance but also introduce invertibility in the dual formulation.
• The square of the 2-norm of the vector of slack variables is used in RKNNWTSVR to make the objective functions strongly convex.
• Four algorithms are designed to solve the proposed RKNNWTSVR.
• The solution reduces to solving just two systems of linear equations which makes our RKNNWTSVR extremely simple and efficient.
• No external optimizer is necessary for solving the RKNNWTSVR formulation.
摘要
•Our RKNNWTSVR implements structural risk minimization principle by introducing extra regularization terms in each objective function.•Our RKNNWTSVR cannot only help to alleviate overfitting issue and improve the generalization performance but also introduce invertibility in the dual formulation.•The square of the 2-norm of the vector of slack variables is used in RKNNWTSVR to make the objective functions strongly convex.•Four algorithms are designed to solve the proposed RKNNWTSVR.•The solution reduces to solving just two systems of linear equations which makes our RKNNWTSVR extremely simple and efficient.•No external optimizer is necessary for solving the RKNNWTSVR formulation.
论文关键词：Machine learning,Support vector machines,Twin support vector machines,K-nearest neighbor,Newton method,Smoothing techniques
论文评审过程：Received 28 March 2015, Revised 10 November 2015, Accepted 12 November 2015, Available online 28 November 2015, Version of Record 7 January 2016.
论文官网地址：https://doi.org/10.1016/j.knosys.2015.11.011
