Commenced in January 2007
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Paper Count: 33122
Multi-Channel Information Fusion in C-OTDR Monitoring Systems: Various Approaches to Classify of Targeted Events
Authors: Andrey V. Timofeev
Abstract:
The paper presents new results concerning selection of optimal information fusion formula for ensembles of C-OTDR channels. The goal of information fusion is to create an integral classificator designed for effective classification of seismoacoustic target events. The LPBoost (LP-β and LP-B variants), the Multiple Kernel Learning, and Weighing of Inversely as Lipschitz Constants (WILC) approaches were compared. The WILC is a brand new approach to optimal fusion of Lipschitz Classifiers Ensembles. Results of practical usage are presented.Keywords: Lipschitz Classifier, Classifiers Ensembles, LPBoost, C-OTDR systems, ν-OTDR systems.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1106893
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[1] K. N. Choi, J. C. Juarez, and H. F. Taylor (2003), Distributed fiber-optic pressure/seismic sensor for low-cost monitoring. SPIE 5090, pp.134- 141.
[2] A.V. Timofeev, D.V. Egorov (2014), Multichannel classification of target signals by means of an SVM ensemble in C-OTDR systems for remote monitoring of extended objects, MVML-2014 Conference Proceedings V.1, Prague.
[3] T. Hofmann, D. Sholkopf, J. Smola (2008) Kernel Methods in machine Learning, Annals of Statistics, V.36, No. 3, pp. 1171-1220.
[4] G. R. G. Lanckriet, N. Cristianini, P. Bartlett, L. E. Ghaoui, and M. I. Jordan. (2004) Learning the kernel matrix with semidefinite programming. JMLR, 5, pp. 27–72.
[5] Jebara, T., Kondor, I., Howard, A. (2004) Probability product kernels. Journal of Machine Learning Research, 5, pp. 819–844.
[6] M. Narasimha Murty, V. S. Devi (2011) Combination of Classifiers in Pattern Recognition (Undergraduate Topics in Computer Science), Vol. 0, (pp. 188-206), London: Springer.
[7] A.V. Timofeev (2012) The guaranteed estimation of the Lipschitz classifier accuracy: Confidence set approach. Journal Korean Stat. Soc. 41(1). pp. 105-114.
[8] U., Luxburg, O. Bousquet (2004) Distance-based classification with Lipschitz functions, Journal of Machine Learning Research Vol. 5:, pp. 669-695.
[9] M. A. Hears, S. T. Dumais, E. Osman, J. Platt, and B. Scholkopf, Support Vector Machines, IEEE Intelligent Systems, vol. 13(4), pp.18- 28, 1998.
[10] F. R. Bach, G. R. G. Lanckriet, and M. I. Jordan (2004) Multiple kernel learning, conic duality, and the SMO algorithm. In ICML.
[11] A. Demiriz, K.P. Bennett, and J. Shawe Taylor (2002) Linear programming boosting via column generation. Machine Learning, vol. 46 (1-3), pp. 225-254.
[12] P. Gehler and S. Nowozin (2009) On feature combination for multiclass object classification. In Proc. ICCV.
[13] J. A. Blimes, A. Gentle (1998) A Tutorial of the EM Algorithm and its Application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models. Tech. Rep., Int'l Computer Science Institute, Berkeley CA. pp. 97-021