ε-tube based pattern selection for support vector machines

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

Volumn 3918 LNAI, Issue , 2006, Pages 215-224

  Kim, Dongil ^a   Cho, Sungzoon ^a  

^a Seoul National University   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL COMPLEXITY; DATABASE SYSTEMS; DISTRIBUTED COMPUTER SYSTEMS; PROBABILITY; STOCHASTIC PROGRAMMING;

PATTERN SELECTION; SUPPORT VECTOR MACHINES; SUPPORT VECTOR REGRESSION (SVR);

PATTERN RECOGNITION;

EID: 33745796773     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11731139_26     Document Type: Conference Paper

References (13)

1. Vapnik, V., The Nature of Statistical Learning Theory, Springer, New York (1995)
2. Cristianini, N., Shawe-Taylor, J., An Introduction to Support Vector Machines, Cambridge University Press, Cambridge, UK (2000)
3. Drucker, H., Burges, C. J. C., Kaufman, L., Smola, A., Vapnik, V., Support Vector Regression Machines, In: Mozer, M. C., Jordan, M. I., Petsche, T.(eds.): Advances in Neural Information Processing System 9, MIT Press, Cambridge, MA (1997) 155-161
4. Platt, J. C., Fast Training of Support Vector Machines Using Sequential Minimal Optimization, Advanced in Kernel Methods; Support Vector Machines, MIT Press, Cambridge, MA (1999) 185-208
5. Shin, H., Cho, S., Pattern Selection for Support Vector Classifiers, Lecture Notes in Computer Science 2412 (2002) 469-474
6. Shin, H., Cho, S., Fast Pattern Selection Algorithm for Support Vector Classifiers: Time Complexity Analysis, Lecture Notes in Computer Science 2690 (2003) 1008-1015
7. Wang, W., Xu, Zongben., A Heuristic Training for Support Vector Regression, Neuro-computing 61 (2004) 259-275
8. Sun, J., Cho, S., Pattern Selection for Support Vector Regression based on Sparseness and Variability, Submitted (2005)
9. Smola, A., Schölkopf, B., A Tutorial on Support Vector Regression, NeuroCOLT Technical Report NC-TR-98-030, Royal Holloway College, University of London, UK (2002)
10. Chalimourda, A., Schölkopf, B., Smola, A., Experimentally Optimal v in Support Vector Regression for Different Noise Models and Parameter Settings, Neural Networks 17 (2004) 127-141
11. Santa Fe Dataset: http://www-psych.stanford.edu/~andreas/Time-Series/ SantaFe.html
12. KOSPI200 Dataset from Korea Stock Market: http://sm.krx.co.kr
13. Cherkassky, V., Ma, Y., Practical Selection of SVM Parameters and Noise Estimation for SVM Regression, Neural Networks 17 (2004) 113-126