M. Davy, A. Gretton, A. Doucet, and P.W.J. Rayner, "Optimised support vector machines for nonstationary signal classification," IEEE transactions on Signal Processing, vol. 9, no. 12, Dec. 2002. Home/Search Document Not in Database Summary Related Articles Check |
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Support Vector Machines and Learning about Time - Rüping, Morik (2003) (Correct)
....kernels proposed by Vapnik in [24] Ch. 11. or the time frequency kernel of 4 Data Tool d=2 d=3 d=4 d=5 AR model AR 0.640 0.624 0.624 0.633 SVM 0.648 0.634 0.632 0.639 AR outliers AR 0.942 0.922 0.919 0.911 SVM 0.885 0.826 0.832 0.827 Figure 1: Performance of AR and SVM model. Davy et al. [7]. Vapnik s kernel is based on the typical kernel trick: While the constructing the Fourier series expansion of a time series is hard, calculating the inner product in the feature space given by the Fourier expansion of order N is easy: K(x i ; x j ) sin( 2N 1 (x i x j ) sin (x i x j ) ....
M. Davy, A. Gretton, A. Doucet, and P.W.J. Rayner. Optimised support vector machines for nonstationary signal classi cation. IEEE transactions on Signal Processing, 9(12), Dec. 2002.
An Online Kernel Change Detection Algorithm - Desobry, Davy (2004) Self-citation (Davy) (Correct)
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M. Davy, A. Gretton, A. Doucet, and P. Rayner, "Optimised support vector machines for nonstationary signal classification," IEEE Signal Processing letters, vol. 9, no. 12, pp. 442--445, December 2002.
An Online Kernel Change Detection Algorithm - Desobry, Davy (2004) Self-citation (Davy) (Correct)
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M. Davy, A. Gretton, A. Doucet, and P. Rayner, "Optimised Support Vector Machines for Nonstationary Signal Classification," IEEE Signal Processing Letters, vol. 9, no. 12, pp. 442--445, Dec. 2002.
Support Vector Machines And Learning About Time - Stefan Uping And (2003) (Correct)
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M. Davy, A. Gretton, A. Doucet, and P.W.J. Rayner, "Optimised support vector machines for nonstationary signal classification," IEEE transactions on Signal Processing, vol. 9, no. 12, Dec. 2002.
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