Measurement-based statistical fusion methods for distributed sensor networks

Distributed Sensor Networks

Volumn , Issue , 2004, Pages 301-320

  Rao, Nageswara S V ^a  

^a OAK RIDGE NATIONAL LABORATORY   (United States)

Author keywords

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Indexed keywords

EID: 85056055649     PISSN: None     EISSN: None     Source Type: Book    
DOI: None     Document Type: Chapter

References (50)

1. Varshney, P.K., Distributed Detection and Data Fusion, Springer-Verlag, 1997.
2. Vapnik, V., Estimation of Dependences Based on Empirical Data, Springer-Verlag, New York, 1982.
3. Grofman, B. and Owen, G. (eds.), Information Pooling and Group Decision Making, Jai Press Inc., Greenwich, CT, 1986.
4. De Laplace, P.S., Deuxiéme supplѐment a´ la thѐorie analytique des probabilitѐs, 1818. Reprinted (1847) in Oeuvres Complétes de Laplace, vol. 7, Gauthier-Villars, Paris, 531.
5. Madan, R.N. and Rao, N.S.V., Guest editorial on information/decision fusion with engineering applications, Journal of Franklin Institute, 336(2), 199, 1999.
6. Von Neumann, J., Probabilistic logics and the synthesis of reliable organisms from unreliable components, in Shannon, C.E. and McCarthy, J. (eds), Automata Studies, Princeton University Press, 1956, 43.
7. Granger, C.W.J., Combining forecasts — twenty years later, Journal of Forecasting, 8, 167, 1989.
8. Chow, C.K., Statistical independence and threshold functions, IEEE Transactions on Electronic Computers, EC-16, 66, 1965.
9. Hashem, S. et al., Optimal linear combinations of neural networks: an overview, in Proceedings of 1994 IEEE Conference on Neural Networks, 1507, 1994.
10. Dasarathy, B.V., Decision Fusion, IEEE Computer Society Press, Los Alamitos, CA, 1994.
11. Breiman, L., Stacked regressions, Machine Learning, 24(1), 49, 1996.
12. Drakopoulos, E. and Lee, C.C., Optimal multisensor fusion of correlated local decision, IEEE Transactions on Aerospace Electronics Systems, 27(4), 593, 1991.
13. Rao, N.S.V., Distributed decision fusion using empirical estimation, IEEE Transactions on Aerospace and Electronic Systems, 33(4), 1106, 1996.
14. Rao, N.S.V., On sample-based implementation of non-smooth decision fusion functions, in IEEE/SICE/RSJ International Conference on Multisensor Fusion and Integration for Intelligent Systems, 2001.
15. Devroye, L. et al., A Probabilistic Theory of Pattern Recognition. Springer-Verlag, New York, 1996.
16. Rao, N.S.V. and Oblow, E.M., Majority and location-based fusers for PAC concept learners. IEEE Transactions on Systems, Man and Cybernetics, 24(5), 713, 1994.
17. Rao, N.S.V. and Oblow, E.M., N-learners problem: System of PAC learners, in Computational Learning Theory and Natural Learning Systems, Vol 5: Making Learning Practical, MIT Press, 1997, 189.
18. Anthony, M., Probabilistic analysis of learning in artificial neural networks: the PAC model and its variants. NeuroCOLT Technical Report Series NC-TR-94-3, Royal Holloway, University of London, 1994.
19. Tang, Z. and Koehler, G.J., Lipschitz properties of feedforward neural networks, Technical report, 1994.
20. Rao, N.S.V., Fusion rule estimation in multiple sensor systems using training, in Modelling and Planning for Sensor Based Intelligent Robot Systems, Bunke, H. et al. (eds), World Scientific, 1995, 179.
21. Rao, N.S.V., Multiple sensor fusion under unknown distributions. Journal of Franklin Institute, 336(2), 285, 1999.
22. Rao, N.S.V., Fusion methods in multiple sensor systems using feedforward neural networks. Intelligent Automation and Soft Computing, 5(1), 21, 1999.
23. Rao, N.S.V., Fusion methods for multiple sensor systems with unknown error densities, Journal of Franklin Institute, 331B(5), 509, 1994.
24. Lugosi, G. and Zeger, K., Nonparametric estimation via empirical risk minimization, IEEE Transactions on Information Theory, 41(3), 677, 1995.
25. Sima, J., Back-propagation is not efficient, Neural Networks, 9(6), 1017, 1996.
26. Rao, N.S.V., Vector space methods for sensor fusion problems, Optical Engineering, 37(2), 499, 1998.
27. Vavasis, S.A., Nonlinear Optimization, Oxford University Press, New York, 1991.
28. Aizerman, M.A. et al., Extrapolative problems in automatic control and method of potential functions, American Mathematical Society Translations, 87, 281, 1970.
29. Rao, N.S.V. et al., Iyengar. Learning algorithms for feedforward networks based on finite samples. IEEE Transactions on Neural Networks, 7(4), 926, 1996.
30. Kurkova, V., Kolmogorov’s theorem and multilayer neural networks, Neural Networks, 5, 501, 1992.
31. Cybenko, G., Approximation by superpositions of a sigmoidal function, Mathematics of Contols, Signals, and Systems, 2, 303, 1989.
32. Ciesielski, Z., Haar system and nonparametric density estimation in several variables, Probability and Mathematical Statistics, 9, 1, 1988.
33. Prakasa Rao, B.L.S., Nonparametric Functional Estimation, Academic Press, New York, 1983.
34. Engel, J., A simple wavelet approach to nonparametric regression from recursive partitioning schemes, Journal of Multivariate Analysis, 49, 242, 1994.
35. Nadaraya, E.A., Nonparametric Estimation of Probability Densities and Regression Curves, Kluwer Academic Publishers, Dordrecht, 1989.
36. Rao, N.S.V. and Protopopescu, V., On PAC learning of functions with smoothness properties using feedforward sigmoidal networks. Proceedings of the IEEE, 84(10), 1562, 1996.
37. Rao, N.S.V., Nadaraya–Watson estimator for sensor fusion, Optical Engineering, 36(3), 642, 1997.
38. Preparata, F.P. and Shamos, I.A., Computational Geometry: An Introduction, Springer-Verlag, New York, 1985.
39. Rao, N.S.V. and Iyengar, S.S., Distributed decision fusion under unknown distributions, Optical Engineering, 35(3), 617, 1996.
40. Rao, N.S.V., A fusion method that performs better than best sensor, in First International Conference on Multisource–Multisensor Data Fusion, 1998, 19.
41. Rao, N.S.V. et al., N-learners problem: fusion of concepts. IEEE Transactions on Systems, Man and Cybernetics, 24(2), 319, 1994.
42. Roychowdhury, V. et al. (eds), Theoretical Advances in Neural Computation and Learning, Kluwer Academic, Boston, 1994.
43. Rao, N.S.V., Finite sample performance guarantees of fusers for function estimators, Information Fusion, 1(1), 35, 2000.
44. Mojirsheibani, M., A consistent combined classification rule. Statistics and Probability Letters, 36, 43, 1997.
45. Hashem, S., Optimal linear combinations of neural networks, Neural Networks, 10(4), 599, 1997.
46. Taniguchi, M. and Tresp, V., Averaging regularized estimators, Neural Computation, 9, 1163, 1997.
47. Rao, N.S.V., Projective method for generic sensor fusion problem, in IEEE/SICE/RSJ International Conference on Multisensor Fusion and Integration for Intelligent Systems, 1999, 1.
48. Rao, N.S.V., On optimal projective fusers for function estimators, in Second International Conference on Information Fusion, 1999, 296.
49. Rao, N.S.V., To fuse or not to fuse: fuser versus best classifier, in SPIE Conference on Sensor Fusion: Architectures, Algorithms, and Applications 2, 34, 1998, 25.
50. Rao, N.S.V., Nearest neighbor projective fuser for function estimation, in Proceedings of International Conference on Information Fusion, 2002.