Minimax probability machine

Advances in Neural Information Processing Systems

Volumn , Issue , 2002, Pages

  Lanckriet, Gert R G ^a   Bhattacharyya, Chiranjib ^a   El Gliaoui, Laurent ^a   Jordan, Michael I ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CONVEX OPTIMIZATION;

DATA POINTS; DECISION BOUNDARY; MERCER KERNEL; MINIMAX PROBABILITY MACHINE; OPTIMIZATION PROBLEMS; PROBABILITY OF CORRECT CLASSIFICATIONS; PROBABILITY OF MISCLASSIFICATION;

OPTIMIZATION;

EID: 84898982587     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (8)

1. Anderson, T. W. and Bahadur, R R, (1962) Classification into two multivariate Normal distributions with different covariance matrices. Annals of Mathematical Statistics 33(2): 420-431.
2. Bertsimas, D. and Sethuraman, J. (2000) Moment problems and semidefinite optimization. Handbook of Semidefinite Optimization 469-509, Kluwer Academic Publishers.
3. Breiman L. (1996) Arcing classifiers. Technical Report 460, Statistics Department, University of California, December 1997.
4. Chernoff H. (1972) The selection of effective attributes for deciding between hypothesis using linear discriminant functions. In Frontiers of Pattern Recognition, (S. Watanabe, ed, ), 55-60. New York: Academic Press.
5. Boyd, S. and Van den berghe, L. (2001) Convex Optimization Course notes for EE364, Stanford University. Available at http://www.stanford.edu/class/ ee364.
6. Isii, K. (1963) On the sharpness of Chebyshev-type inequalities. Ann. Inst. Stat. Math. 14: 185-197.
7. Mika, M, Ratsch, G., Weston, J., Scholkopf, B and Miiller, K.-R. (1999) Fisher discriminant analysis with kernels. In Neural Networks for Signal Processing IX, 41-48, New York: IEEE Press.
8. Nesterov, Y. and Nemirovsky, A. (1994) Interior Point Polynomial Methods in Convex Programming: Theory and Applications. Philadelphia, PA: SIAM.