Support vector regression hybrid algorithm based on rough set

Zhongguo Shiyou Daxue Xuebao (Ziran Kexue Ban)/Journal of China University of Petroleum (Edition of Natural Science)

Volumn 33, Issue 5, 2009, Pages 159-163

  Deng, Jiu Ying ^a,b   Wang, Qin Ruo ^a   Mao, Zong Yuan ^c   Du, Qi Liang ^c  

^a GUANGDONG UNIVERSITY OF TECHNOLOGY   (China)

^b GUANGDONG UNIVERSITY OF EDUCATION   (China)

^c SOUTH CHINA UNIVERSITY OF TECHNOLOGY   (China)

Author keywords

Algorithm of SMO regression; Boundary set; Rough set; Support vector regression

Indexed keywords

BOUNDARY SAMPLES; BOUNDARY SET; HIGH EFFICIENCY; HIGH QUALITY; HYBRID ALGORITHMS; IMPRECISE DATA; LEARNING MACHINES; MACHINE-LEARNING; ROUGH SET; SEQUENTIAL MINIMAL OPTIMIZATION ALGORITHMS; SMO ALGORITHMS; SUPPORT VECTOR; SUPPORT VECTOR REGRESSION; SUPPORT VECTOR REGRESSIONS; TRAINING SETS; TRAINING SUBSETS;

FUZZY SETS; LEARNING SYSTEMS; RAPID SOLIDIFICATION; REGRESSION ANALYSIS; ROUGH SET THEORY; SEQUENTIAL SWITCHING; VECTORS;

LEARNING ALGORITHMS;

EID: 70749090812     PISSN: 16735005     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (11)

1. VAPNIK V N. Support vector method[J]. Lecture Notes in Computer Science, 1997(1): 1327: 263.
2. VAPNIK Vladimir N. 2004: 6.
3. XU Chang-hang, CHEN Guo-ming, XIE Jing. Structural reliability analysis method based on support vector machines and Monte Carlo and its application[J]. Journal of China University of Petroleum(Edition of Natural Science), 2008, 32(4): 103-108.
4. PLATT J. Sequential minimal optimization: a fast algorithm for training support vector machines[R/OL]. Tech Rep MSR-TR 98-14, Microsoft Res., Redmond, WA, Apr. 1998[2007-09-30]. http://research.microsoft.com/en-us/um/people/jplatt/smotr.pdf.
5. FLAKE Gary William. Steve Lawrence efficient SVM regression training with SMO[J]. Machine Learning, 2002, 46: 271-290.
6. YANG Jie, YE Chen-zhou, QUAN Yong, et al. Simplified SMO algorithm for support vector regression[J]. Infrared and Laser Engineering, 2004, 10(33): 371-375.
7. SHEVADE S K, KEERTHI S S, BHATTACHARYYA C, et al. Improvments to the SMO algorithm for SVM regression[J]. IEEE Transactions on Neural Networks, 2000, 9(11): 1188-1193.
8. MATTERA D, PALMIERI F, HAYKIN S. An explicit algorithm for training support vector machines[J]. Signal Processing Letters, IEEE, 1999, 9(6): 243-245.
9. ZENG Shao-hua, WEI Yan, DUAN Ting-cai, et al. Research on algorithm of support vector stepwise regression[J]. Computer Engineering and Applications, 2007, 43(8): 78-81.
10. 2006: 77-95.
11. 2001: 24-34.