Simplified SMO algorithm for support vector regression

Hongwai yu Jiguang Gongcheng/Infrared and Laser Engineering

Volumn 33, Issue 5, 2004, Pages 533-537

  Yang, Jie ^a   Ye, Chen Zhou ^a   Quan, Yong ^a   Chen, Nian Yi ^a  

^a SHANGHAI JIAO TONG UNIVERSITY   (China)

Author keywords

Sequential Minimal Optimization; Support Vector Classification; Support Vector Regression

Indexed keywords

RISK MINIMIZATION; SEQUENTIAL MINIMAL OPTIMIZATION (SMO); SUPPORT VECTOR CLASSIFICATION; SUPPORT VECTOR REGRESSION (SVR);

ALGORITHMS; IMAGE PROCESSING; OPTIMIZATION; PATTERN RECOGNITION; REGRESSION ANALYSIS; RISK ASSESSMENT;

VECTOR QUANTIZATION;

EID: 15844367263     PISSN: 10072276     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (6)

1. Vapnik V. The Nature of Statistical Learning Theory. 2nd ed. [M]. New York:Springer-Verlag, 1998.
2. Platt J C. Fast Training of Support Vector Machines Using Sequential Minimal Optimization[M]. Advances in Kernel Methods:Support Vector Machines (Edited by Scholkopf B, Surges C,Smola A)[M]. Cambridge MA: MIT Press, 1998. 185-208.
3. Smola Alex J, Scholkopf Bernhard. A Tutorial on Support Vector Regression[EB/OL]. http://www. neurocolt.com NeuroCOLT2 Technical Report Series NC2-TR-1998-030.
4. Flake Gary William,Lawrence Steve. Efficient SVM Regression Training with SMO[J]. Machine Learning,2002,46(1/2/3).271-290.