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I-95 Expert Estimations 35 predicted additional fill (ft) 30 25 20 15 10 5 0 1 2 3 4 expert 5 6 7 average best estimate = 15.6 ft. actual fill added at failure = 18.7 ft.

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Hatzinakos, Biometric methods for secure communications in body sensor networks: Resource-ef cient key management and signal-level data scrambling, EURASIP J Adv Signal Processing, vol 2008:1 16, Article ID 529879, 2008, doi:101155/2008/529879 35 J P Martinez et al, A wavelet-based ECG delineator: Evaluation on standard databases, IEEE Trans Biomed Eng, 51(4):570 581, 2004 36 J Lu, Discriminant Learning for Face Recognition, PhD thesis, University of Toronto, 2004..

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37 Y Guermeur, A Lifchitz, and R Vert, A kernel for protein secondary structure prediction, in Kernel Methods in Computational Biology, The MIT Press, Cambridge, MA, 2004, pp 193 206 38 N Cristianini, J Kandola, A Elisseeff, and J Shawe-Taylor, On kernel target alignment, in Advances in Neural Information Processing Systems, The MIT Press, Cambridge, MA, 2001 39 C Park and H Park, A relationship between LDA and the generalized minimum squared error solution, SIAM J Matrix Anal Appl 27(2):474 492, 2005 40 J Ye, Least squares linear discriminant analysis, in Proceedings of the 24th International Conference on Machine Learning, 2007, pp 1087 1093 41 O Chapelle, B Sch lkopf, and A Zien, editors Semi-Supervised Learning MIT Press, Cambridge, o MA, 2006 42 D Zhou, O Bousquet, T Lal, J Weston, and B.

Figure 21.2 MIT I-95 embankment predictors 50% con dence ranges of added height to cause failure (after Hynes, M. and Vanmarke, E., 1977, Reliability of Embankment Performance Predictions, Proceedings ASCE Engineering Mechanics Division Specialty Conference. Mechanics in Engineering, University of Waterloo Press, pp. 367 384, reproduced with permission of University of Waterloo Press).

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Sch lkopf, Learning with local and global consistency, o in Advances in Neural Information Processing Systems, 2003, pp 321 328 43 X Zhu, Z Ghahramani, and J Lafferty, Semi-supervised learning using Gaussian elds and harmonic functions, in Proceedings of the 20th International Conference on Machine Learning, 2003, pp 912 919 44 J Chen, J Ye, and Q Li, Integrating global and local structures: A least squares framework for dimensionality reduction, in IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2007, pp 1 8 45 M Belkin and P Niyogi, Laplacian eigenmaps and sepctral techniques for embedding and clustering, Adv Neural Inf Processing Sys 15:585 591, 2001 46 N Cristianini and J S Taylor, An Introduction to Support Vector Machines and other Kernel-Based Learning Methods, Cambridge University Press, New York, 2000 47 S Sch lkopf and A.

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Smola, Learning with Kernels: Support Vector Machines,Regularization, Optio mization and Beyond, MIT Press, Cambridge, MA, 2002 48 J Shawe-Taylor and N Cristianini, Kernel Methods for Pattern Analysis, Cambridge University Press, New York, 2004 49 S Mika, G R tsch, J Weston, B Sch lkopf, and K-R M ller, Fisher discriminant analysis with a o u kernels, in Y-H Hu, J Larsen, E Wilson, and S Douglas, editors, Neural Networks for Signal Processing IX, IEEE, New York, 1999, pp 41 48 50 B Sch lkopf, A J Smola, and K-R M ller, Nonlinear component analysis as a kernel eigenvalue o u problem, Neural Comput 10(5):1299 1319, 1998 51 G Baudat and F Anouar, Generalized discriminant analysis using a kernel approach, Neural Comput 12(10):2385 2404, 2000 52 S Mika, G R tsch, J Weston, B Sch lkopf, A Smola, and K-R.

I-95 Expert Estimations 35 predicted additional fill (ft) 30 25 20 15 10 5 0 1 2 3 4 expert 5 6 7 average best estimate = 15.6 ft. actual fill added at failure = 18.7 ft.

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