Weighted Lagrange ε-twin support vector regression
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- 作者：Ya-Fen Yea ; bAuthor Vitae ; Lan BaicAuthor Vitae ; Xiang-Yu HuadAuthor Vitae ; Yuan-Hai Shaoa ; Author Vitae ; Zhen WangcAuthor Vitae ; Nai-Yang Denge ; Author Vitae
- 关键词：Regression ; ε-twin support vector machine ; Weighted coefficient ; Quadratic cost function ; Lagrangian functions
- 刊名：Neurocomputing
- 出版年：2016
- 出版时间：12 July 2016
- 年：2016
- 卷：197
- 期：Complete
- 页码：53-68
- 全文大小：1470 K
文摘
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Weighted Lagrangian ε-twin support vector regression (WL-ε-TSVR) is proposed.
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Weight matrix D is introduced to reduce the impact of outliers.
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WL-ε-TSVR just needs to solve the simple unconstrained minimization problems (UMPs).
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A linearly convergent Lagrangian algorithm is used to obtain the solutions of UMPs.
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Experimental results indicate that WL-ε-TSVR has remarkably improved generalization performance.