A generic sensor fusion problem: Classification and function estimation

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

Volumn 3077, Issue , 2004, Pages 16-30

  Rao, Nageswara S V ^a  

^a OAK RIDGE NATIONAL LABORATORY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER SCIENCE; COMPUTERS;

DISTRIBUTION-FREE; EMPIRICAL RISK MINIMIZATION; FUNCTION ESTIMATION; GENERIC SENSORS; INPUT-OUTPUT; MULTIPLE SENSORS; PERFORMANCE BOUNDS; SENSOR MEASUREMENTS;

ARTIFICIAL INTELLIGENCE;

EID: 35048820961     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-25966-4_2     Document Type: Article

References (23)

1. C. K. Chow. Statistical independence and threshold functions. IEEE Trans. Electronic Computers, EC-16:66-68, 1965.
2. L. Devroye, L. Gyorfi, and G. Lugosi. A Probabilistic Theory of Pattern Recognition. Springer-Verlag, New York, 1996.
3. G. Giacinto and F. Roli. Dynamic classifier selection based on multiple classifier behavior. Pattern Recognition, 34:1879-1881, 2001.
4. B. Grofman and G. Owen, editors. Information Pooling and Group Decision Making. Jai Press Inc., Greenwich, Connecticut, 1986.
5. J. Kittler and F. Roli, editors. Multiple Classifier Systems, volume 1857. Springer-Verlag, Berlin, 2000.
6. V. Kurkova. Kolmogorov's theorem and multilayer neural networks. Neural Networks, 5:501-506, 1992.
7. R. N. Madan and N. S. V. Rao. Guest editorial on information/decision fusion with engineering applications. Journal of Franklin Institute, 336B(2), 1999. 199-204.
8. A. Nobel. Histogram regression estimation using data-dependent partitions. Annals of Statistics, 24(3):1084-1105, 1996.
9. N. S. V. Rao. Distributed decision fusion using empirical estimation. IEEE Transactions on Aerospace and Electronic Systems, 33(4):1106-1114, 1996.
10. N. S. V. Rao. Fusion methods in multiple sensor systems using feedforward neural networks. Intelligent Automation and Soft Computing, 5(1):21-30, 1998.
11. N. S. V. Rao. To fuse or not to fuse: Fuser versus best classifier. In SPIE Conference on Sensor Fusion: Architectures, Algorithms, and Applications II, pages 25-34. 1998.
12. N. S. V. Rao. Vector space methods for sensor fusion problems. Optical Engineering, 37(2):499-504, 1998.
13. N. S. V. Rao. Multiple sensor fusion under unknown distributions. Journal of Franklin Institute, 336(2):285-299, 1999.
14. N. S. V. Rao. On optimal projective fusers for function estimators. In Second International Conference on Information Fusion, pages 296-301. 1999.
15. N. S. V. Rao. Projective method for generic sensor fusion problem. In IEEE/SICE/RSJ International Conference on Multisensor Fusion and Integration for Intelligent Systems, pages 1-6. 1999.
16. N. S. V. Rao. Finite sample performance guarantees of fusers for function estimators. Information Fusion, 1(1):35-44, 2000.
17. N. S. V. Rao. On fusers that perform better than best sensor. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(8):904-909, 2001.
18. N. S. V. Rao. Multisensor fusion under unknown distributions: Finite sample performance guarantees. In A. K. Hyder, editor, Multisensor Fusion. Kluwer Academic Pub., 2002.
19. N. S. V. Rao. Nearest neighbor projective fuser for fucntion estimation. In Proceedings of International Conference on Information Fusion, 2002.
20. N. S. V. Rao and S. S. Iyengar. Distributed decision fusion under unknown distributions. Optical Engineering, 35(3):617-624, 1996.
21. V. N. Vapnik. The Nature of Statistical Learning Theory. Springer-Verlag, New York, 1995.
22. P. K. Varshney. Distributed Detection and Data Fusion. Springer-Verlag, 1997.
23. T. Windeatt and F. Roli, editors. Multiple Classifier Systems. Springer-Verlag, Berlin, 2003.