Advanced Search
Volume 30 Issue 2
Jan.  2011
Turn off MathJax
Article Contents
Chen Xiao-feng, Wang Shi-tong, Cao Su-qun. SVR with Adaptive Error Penalization[J]. Journal of Electronics & Information Technology, 2008, 30(2): 367-370. doi: 10.3724/SP.J.1146.2006.01081
Citation: Chen Xiao-feng, Wang Shi-tong, Cao Su-qun. SVR with Adaptive Error Penalization[J]. Journal of Electronics & Information Technology, 2008, 30(2): 367-370. doi: 10.3724/SP.J.1146.2006.01081

SVR with Adaptive Error Penalization

doi: 10.3724/SP.J.1146.2006.01081
  • Received Date: 2006-07-20
  • Rev Recd Date: 2007-01-31
  • Publish Date: 2008-02-19
  • A novel support vector regression method AEPSVR is proposed in this paper. First, an approximate regression function is obtained using -SVR method, and then a new adaptive error penalization function is introduced to enhance the robust performance of SVR such that a robust support vector regression is derived. Because the proposed AEPSVR here is based on -SVR, so various optimization methods for SVR can be used. Experimental results show that the proposed AEPSVR can reduce the affect of outliers, and have the very good generalization capability.
  • loading
  • Smola A J and Schkopf B. A tutorial on support vectorregression[J].Statistics and Computing.2004, 14(3):199-222[2]Schokopf B, Smola A J and Williamson R C, et al.. Newsupport vector algorithm[J].Neural Computation.2000, 12(12):1207-1245[3]Song Q, Hu W, and Xie W. Robust support vector machinewith bullet hole image classification[J].IEEE Trans. on Systems,Man and Cybernetics C.2002, 32(4):440-448[4]Weston J and Herbrich R. Adaptive margin support vectormachines. Neural Information Processing Systems (NIPS)Conference Workshop on Advance in Large MarginClassifiers, Breckenridge Colorado USA, 1998: 281-296.[5]Xu Linli, Crammer K, and Schuurmans D. Robust supportvector machine training via convex outlier ablation. InProceedings of the 21st National Conference on ArtificialIntelligence(AAAI-06), Boston USA, 2006: 536-546.[6]Zhan Yiqiang and Shen Dinggang. An adaptive errorpenalization method for training an efficient and generalizedSVM[J].Pattern Recognition.2006, 39(3):342-350[7]张讲社,郭高. 加权稳健支撑向量回归方法. 计算机学报,2005, 28(7): 1171-1177.Zhang Jiang-she and Guo Gao. Reweighted robust supportvector regression method. Chinese Journal of Computer, 2005,28(7): 1171-1177.[8]Suykens J A K, De Brahanter J, Lukas L, and Vandewalle J.Weighted least squares support vector machine: robustnessand sparse approximation[J].Neurocomputing.2002, 48(1-4):85-105[9]Chuang C C, Su F F, Jeng J T, and Hsiao C C. Robustsupport regression networks for function approximation withoutliers[J].IEEE Trans. on Neural Networks.2002, 13(6):1322-1330[10]Yong Zhan and HaoZhong Cheng. A robust support vectoralgorithm for harmonic and interharmonic analysis of electricpower system[J].Electric Power Systems Research.2005, 73(3):393-400[11]Wang Shitong, Zhu Jiagang and Chung Fu-Lai, et al..Experimental study on parameter choices in norm-r supportvector regression machines with noisy input[J].Soft Computing.2006, 10(3):219-223[12]Wang Shitong, Zhu Jiagang and Chung Fu-Lai, et al..Theoretically optimal parameter choices for support vectorregression machines with noisy input[J].Soft Computing.2005,9(10):732-741
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (3111) PDF downloads(858) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return