A novel sequential minimal optimization algorithm for support vector regression

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4232 LNCS, Issue , 2006, Pages 827-836

  Guo, Jun ^a   Takahashi, Norikazu ^a   Nishi, Tetsuo ^b  

^a KYUSHU UNIVERSITY   (Japan)

^b WASEDA UNIVERSITY   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

LEARNING SYSTEMS; OPTIMIZATION; PROBLEM SOLVING; QUADRATIC PROGRAMMING; REGRESSION ANALYSIS;

CONVEX OPTIMIZATION PROBLEMS; SEQUENTIAL MINIMAL OPTIMIZATION (SMO) ALGORITHMS; SUPPORT VECTOR REGRESSION;

LEARNING ALGORITHMS;

EID: 33750600332     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11893028_92     Document Type: Conference Paper

References (6)

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3. Srnola, A. J., Schölkopf, B.: A tutorial on support vector regression. Technical Report NC2-TR-1998-030, NeuroCOLT2 (1998)
4. Shevade, S. K., Keerthi, S. S., Bhattacharyya, C., Murthy, K. R. K.: Improvements to the SMO algorithm for SVM regression. IEEE Trans. Neural Networks 11-5 (2000) 1188-1193
5. Flake, G. W., Lawrence, S.: Efficient SVM regression training with SMO, Machine Learning 46 (2002) 271-290
6. Guo, J., Takahashi, N., Nishi, T.: Convergence proof of a sequential minimal optimization algorithm for support vector regression. In: Proc. of IJCNN'06 (2006)