SLMBSVMs: A structural-loss-minimization-based support vector machines approach

Proceedings of 2002 International Conference on Machine Learning and Cybernetics

Volumn 3, Issue , 2002, Pages 1455-1459

  Zhang, Liang ^a   Yu, Shui ^a   Ye, Yun Ming ^a   Ma, Fan Yuan ^a  

^a SHANGHAI JIAO TONG UNIVERSITY   (China)

Author keywords

Classification; Structural loss minimization; Support vector machines (SVMs)

Indexed keywords

LEARNING ALGORITHMS; MATHEMATICAL MODELS; PROBLEM SOLVING; REGRESSION ANALYSIS;

GENERALIZATION ERROR LOSS; STRUCTURAL LOSS MINIMIZATION; STRUCTURAL RISKS; SUPPORT VECTOR MACHINES;

LEARNING SYSTEMS;

EID: 0036931039     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (10)

1. V. N. Vapnik, The Nature of Statistical Learning Theory. New York: Springer-Verlag, 1995.
2. Statistical Learning Theory. New York: Wiley, 1998.
3. B. Schölkopf, C. Burges, and A. Smola, Advances in Kernel Methods: Support Vector Learning. Cambridge, MA: MIT Press, 1999.
4. C. Burges, "A tutorial on support vector machines for pattern recognition," Data Mining and Knowledge Discovery, vol. 2, no. 2, 1998.
5. C. Cortes and V. N. Vapnik, "Support vector networks," Machine Learning, vol. 20, pp. 273-297, 1995.
6. O. L. Mangasarian David R. Musicant. Active Support Vector Machine Classification
7. M. Pontil and A. Verri, Massachusetts Inst. Technol., 1997, AI Memo no. 1612. Properties of support vector machines.
8. Chun-Fu Lin and Sheng-De Wang. Fuzzy Support Vector Machines IEEE Transactions on Neural Networks, Vol. 13, No. 2, March 2002
9. E. Osuna, R. Freund, and F. Girosi, Support vector machines: Training and applications. A.I. Memo 1602, MIT A. I. Lab., 1997.
10. B. Schölkopf, C. Burges, and V. Vapnik. Extracting support data for a given task. In U.M. Fayyad and R. Uthurusamy, editors, Proceedings of the First International Conference on Knowledge Discovery and Data Mining, Menlo Park, CA, 1995. AAAI Press.