Reweighted robust support vector regression method

Jisuanji Xuebao/Chinese Journal of Computers

Volumn 28, Issue 7, 2005, Pages 1171-1177

  Zhang, Jiang She ^a   Guo, Gao ^a  

^a XI'AN JIAOTONG UNIVERSITY   (China)

Author keywords

Outlier; Robust support vector regression method; Soft pruning; Statistical learning; Support vector machines

Indexed keywords

COMPUTER SIMULATION; LEAST SQUARES APPROXIMATIONS; NUMERICAL ANALYSIS; ROBUSTNESS (CONTROL SYSTEMS); STATISTICAL METHODS; VECTOR QUANTIZATION;

ROBUST SUPPORT VECTOR REGRESSION METHOD; SOFT PRUNING; STATISTICAL LEARNING; SUPPORT VECTOR MACHINES;

REGRESSION ANALYSIS;

EID: 23044501770     PISSN: 02544164     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (7)

1. Vapnik V. N.. The Nature of Statistical Learning Theory. New York: Springer, 1999 (in Chinese)
2. Smola A., Scholkopf B.. A tutorial on support vector regression. Royal Holloway College, London, U.K.: Neuro COLT Technical Report TR-1998-030, 1998
3. Duan K., Keerthi S. S., Poo A. N.. Evaluation of simple performance measures for tunning SVM hyperparameters. National University of Singapore, Singapore: Technical Report CD-01-11, 2001
4. Suykens J. A. K., de Brabanter J., Lukas L., Vandewalle J.. Weighted least squares support vector machines: Robustness and sparse approximation. Neurocomputing, 2002, 48(1-4): 85-105
5. Chuang C.-C., Su F.-F., Jeng J.-T., Hsiao C.-C.. Robusi support vector regression networks for function approximation with outliers. IEEE Transactions on Neural Networks, 2002. 13(6): 1322-1330
6. Platt J. C.. Fast training of support vector machines using sequential optimization. In: Scholkopf B., Burges C., Smola A. eds. Advances in Kernel Methods: Support Vector Machines, Cambridge, MA: MIT Press, 1998, 185-208
7. Shevade S. K., Keerthi S. S., Bhattacharyya C., Murthy K. K. K.. Improvements to SMO algorithm for SVM regression. IEEE Transactions on Neural Networks, 2000, 11(5): 1188-1193