Support vector classification with input data uncertainty

Advances in Neural Information Processing Systems

Volumn , Issue , 2005, Pages

  Bi, Jinbo ^a   Zhang, Tong ^b  

^a SIEMENS CORPORATE RESEARCH   (United States)

^b IBM T J WATSON RESEARCH CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

GEOMETRIC INTERPRETATION; INPUT DATAS; LEARNING MODELS; STATISTICAL FRAMEWORK; STATISTICAL REASONING; SUPPORT VECTOR CLASSIFICATION;

INPUT OUTPUT PROGRAMS;

EID: 84898996224     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (8)

1. J. Bezdek and R. Hathaway. Convergence of alternating optimization. Neural, Parallel Sci. Comput., 11:351-368, 2003.
2. C. Bhattacharyya, K.S. Pannagadatta, and A. J. Smola. A second order cone programming formulation for classifying missing data. In NIPS, Vol 17, 2005.
3. J. Bi and V. N. Vapnik. Learning with rigorous support vector machines. In M. Warmuth and B. Scholkopf, editors, Proceedings of the 16th Annual Conference on Learning Theory, pages 35-42, Menlo Park, CA, 2003. AAAI Press.
4. L. El Ghaoui and H. Lebret. Robust solutions to least-squares problems with uncertain data. SIAM Journal on Matrix Analysis and Applications, 18:1035-1064, 1997.
5. G. H. Golub, P. C. Hansen, and D. P. O'Leary. Tikhonov regularization and total least squares. SIAM Journal on Numerical Analysis, 30:185-194, 1999.
6. G. H. Golub and C. F. Van Loan. An analysis of the total least squares problem. SIAM Journal on Numerical Analysis, 17:883-893, 1980.
7. S. Van Huffel and J. Vandewalle. The Total Least Squares Problem: Computational Aspects and Analysis, in Frontiers in Applied Mathematics 9. SIAM Press, Philadelphia, PA, 1991.
8. V. N. Vapnik. Statistical Learning Theory. John Wiley & Sons, Inc., New York, 1998.