模式识别与人工智能
Saturday, May. 3, 2025 Home      About Journal      Editorial Board      Instructions      Ethics Statement      Contact Us                   中文
  2008, Vol. 21 Issue (1): 1-5    DOI:
Papers and Reports Current Issue| Next Issue| Archive| Adv Search |
Robust Prediction Model of Least Squares Support Vector Machine Based on Sliding Window
ZHAO YongPing, SUN JianGuo
College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016

Download: PDF (500 KB)   HTML (1 KB) 
Export: BibTeX | EndNote (RIS)      
Abstract  In this paper, the mathematical model of weighted least squares support vector machine (WLSSVM) is introduced. Based on the algorithms of heuristic learning and sliding window, a mathematical model of robust prediction of least squares support vector machine (LSSVM) using sliding window is proposed. with the modified heuristic learning algorithm, the strategy of iterative computing matrix inverse is employed to reduce the predicted time without loss of accuracy. Finally, two examples have proved that the proposed model can eliminate the outliers, realize robust prediction and achieve good results.
Key wordsWeighted Least Squares Support Vector Machine (WLSSVM)      Sliding Window      Robustness      Outlier     
Received: 05 March 2007     
ZTFLH: TP274  
Service
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
RSS
Articles by authors
ZHAO YongPing
SUN JianGuo
Cite this article:   
ZHAO YongPing,SUN JianGuo. Robust Prediction Model of Least Squares Support Vector Machine Based on Sliding Window[J]. , 2008, 21(1): 1-5.
URL:  
http://manu46.magtech.com.cn/Jweb_prai/EN/      OR     http://manu46.magtech.com.cn/Jweb_prai/EN/Y2008/V21/I1/1
Copyright © 2010 Editorial Office of Pattern Recognition and Artificial Intelligence
Address: No.350 Shushanhu Road, Hefei, Anhui Province, P.R. China Tel: 0551-65591176 Fax:0551-65591176 Email: bjb@iim.ac.cn
Supported by Beijing Magtech  Email:support@magtech.com.cn