Support vector machine classifiers for sequential decision problems

Proceedings of the IEEE Conference on Decision and Control

Volumn , Issue , 2009, Pages 2558-2563

  Rodríguez Díaz, Eladio ^b   Castañón, David A ^a  

^a Boston University   (United States)

^b BOSTON UNIVERSITY SCHOOL OF MEDICINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CONVEX OPTIMIZATION; DECISION THEORY; SUPPORT VECTOR MACHINES;

ADDITIONAL DATUM; COORDINATE ASCENT; CRITICAL APPLICATIONS; HIGH RELIABILITY; HYPERSPECTRAL MEASUREMENTS; OPTIMIZATION PROBLEMS; SEQUENTIAL DECISIONS; SUPPORT VECTOR MACHINE CLASSIFIERS;

CLASSIFICATION (OF INFORMATION);

EID: 77950786676     PISSN: 07431546     EISSN: 25762370     Source Type: Conference Proceeding    
DOI: 10.1109/CDC.2009.5400391     Document Type: Conference Paper

References (30)

1. Chow, C., "On optimum recognition error and reject tradeoff," IEEE Transactions on Information Theory, Vol.16, No.1, 41-46 (1970).
2. Golfarelli, M., Maio, D., Malton, D., "On the error-reject trade-off in biometric verification systems," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.19, No.7, 786-796 (1997).
3. Cordella, L. P., De Stefano, C., Tortorella, F., Vento, M., "A method for improving classification reliability of multilayer perceptrons," IEEE Trans Neural Networks, Vol.6, No.5, 1140-1147 (1995).
4. Hansen, L. K., Liisberg, C., Salamon, P., "The Error-Reject Tradeoff," Open Systems and Information Dynamics, Vol.4, No.2, 159-184 (1997).
5. Fumera, G., Roli, F., Giacinto, G., "Reject option with multiple thresholds," Pattern Recognition, Vol.33, No.12, 2099-2101 (2000).
6. Fumera, G., Roli, F., Giacinto, G., "Multiple Reject Thresholds for Improving Classification Reliability," Proc. Of the Joint IAPR Int. Workshops on Syntactical and Structural Pattern Recognition and Statistical Pattern Recognition, Alicante, Spain, Lecture Notes in Computer Science, 863-871 (2000).
7. Tortorella, F., "An Optimal Reject Rule for Binary Classifiers," Lecture Notes in Computer Science, 611-620 (2000).
8. Tortorella, F., "A ROC-based reject rule for dichotomizers," Pattern Recognition Letters, Vol.26, No.2, 167-180 (2005).
9. Dubuisson, B., Masson, M., "A statistical decision rule with incomplete knowledge about classes," Pattern Recognition, Vol.26, No.1, 155-165 (1993).
10. Muzzolini, R., Yang, Y. H., Pierson, R., "Classifier design with incomplete knowledge," Pattern Recognition, Vol.31, No.4, 345-369 (1998).
11. De Stefano, C., Sansone, C., Vento, M., "To reject or not to reject: that is the question-an answer in case of neural classifiers," IEEE Transactions on Systems, Man and Cybernetics, Part C, Vol.30, No.1, 84-94 (2000).
12. Ha, T. M., "The optimum class-selective rejection rule," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.19, No.6, 608-615 (1997).
13. Duda, R. O., Hart, P. E., Stork, D. G. Pattern Classification, John Wiley, New York, 2001.
14. Vapnik, V. N. Statistical Learning Theory, John Wiley, New York, 1998.
15. Burges, C. J. C., "A Tutorial on Support Vector Machines for Pattern Recognition," Data Mining and Knowledge Discovery, Vol.2, No.2, 121-167 (1998).
16. Cortes, C., Vapnik, V., "Support-vector networks," Machine Learning, Vol.20, No.3, 273-297 (1995).
17. Mukherjee, S., Tamayo, P., Slonim, D., Verri, A., Golub, T., Mesirov, J., Poggio, T., "Support vector machine classification of microarray data," CBCL Paper, Vol.182, (1999).
18. David, A., Lerner, B., "Support vector machine-based image classification for genetic syndrome diagnosis," Pattern Recognition Letters, Vol.26, No.8, 1029-1038 (2005).
19. Tortorella, F., Cassino, I., "A ROC-Based Reject Rule for Support Vector Machines," Lecture Notes in Computer Science, 106-120 (2003).
20. Yuan, C., Casasent, D., "A Novel Support Vector Classifier with Better Rejection Performance," IEEE Computer Society Conferences on Computer Vision and Pattern Recognition, Vol.1, (2003).
21. Rodríguez-Díaz, E., Pattern Recognition Algorithms for Diagnostic/Screening of Cancer Using Elastic Scattering Spectroscopy, Ph. D. Thesis, Electrical and Computer Engineering, Boston University, May 2009.
22. Platt, J., "Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods," Advances in Large Margin Classifiers, MIT Press, Vol.1, 61-74 (1999).
23. Fumera, G., Roli, F., "Support Vector Machines with Embedded Reject Option," Proceedings of the Int. Workshop on Pattern Recognition with Support Vector Machines (SVM2002), Niagara Falls, Canada, (2002).
24. Fumera, G., Roli, F., "Cost-sensitive learning in support vector machines," VIII Convegno Associazione Italiana Per Lintelligenza Artificiale, (2002).
25. Fletcher, R. Practical Methods of Optimization, Wiley-Interscience, New York, 2000.
26. Bertsekas, D. P. Nonlinear Programming, Athena Scientific, Belmont, Mass., 1999.
27. Platt, J., "Sequential minimal optimization: A fast algorithm for training support vector machines," Advances in Kernel Methods-Support Vector Learning, 185-208 (1999).
28. Shor, N. Z. Minimization Methods for Non-Differentiable Functions, Springer- Verlag, Berlin; New York, 1985.
29. Bigio, I. J., Mourant, J. R.,"Ultraviolet and visible spectroscopies for tissue diagnostics: fluorescence spectroscopy and elastic-scattering spectroscopy," Phys Med Biol, Vol.42, No.5, 803-814 (1997).
30. Johnson, K. S., Chicken, D. W., Pickard, D. C., Lee, A. C., Briggs, G., Falzon, M., Bigio, I. J., Keshtgar, M. R., et al., "Elastic scattering spectroscopy for intraoperative determination of sentinel lymph node status in the breast,"J Biomed Opt, Vol.9, No.6, 1122-1128 (2004).