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A Face Recognition System for Access Control using Video ...
mi.eng.cam.ac.uk/reports/svr-ftp/arandjelovic_PR06.pdf21 Dec 2005: related method of Riklin-Raviv and Shashua [24]. On the other hand, the 3D mor-. ... mateµ̂(Di) is given by the following expression:. µ̂(Di) =. j. αjG(Di; µj, σj), (24). -
MORTGAGE DEFAULT: CLASSIFICATION TREES ANALYSIS David Feldman* and…
mi.eng.cam.ac.uk/~mjfg/local/4F10/Feldman_Gross.pdf15 Nov 2005: MORTGAGE DEFAULT: CLASSIFICATION TREES ANALYSIS. David Feldman and Shulamith Gross#. March 24, 2003. ... 24. robust Cross-Validation method. Since it does not have any particular. -
lf3.dvi
mi.eng.cam.ac.uk/~mjfg/local/4F10/lum.pdf11 Nov 2005: 1994). Dynamic be-lief networks for discrete monitoring. IEEETransactions on Systems, Man, and Cybernet-ics, 24(11):1593{1610. -
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mi.eng.cam.ac.uk/reports/svr-ftp/liao_interspeech05.pdf26 Sep 2005: Uncertainty 1 4 16 256. an — 33.2. LICENo. 24.6 20.7 17.0 12.3CMLLR 16.3 15.3 12.8 13.5. -
report.dvi
mi.eng.cam.ac.uk/reports/svr-ftp/liao_tr499.pdf26 Sep 2005: 23. 4.2 The Conditional Corrupted Speech Distribution. 24. 4.3 Gaussian Mixture Model Approximations. ... of the non-linearity. Alternatively with PMC [24], Gales applies various approximations in the. -
The Cambridge University March 2005 Speaker Diarisation System
mi.eng.cam.ac.uk/reports/full_html/sinha_eurospeech05.html/22 Sep 2005: The experiments reported in this paper use a development set of 24 US broadcast news shows, denoted. -
The Cambridge University March 2005 Speaker Diarisation System R. ...
mi.eng.cam.ac.uk/reports/full_html/sinha_eurospeech05.html/paper.pdf22 Sep 2005: 15. 16. 17. 18. SID CLR Threshold. Dev. 24 D. ER. ... Seg. men. t Im. pu. rity. on. dev. 04 (. 24 s. -
Rapid, Easy and ReliableCalibration for Freehand 3D Ultrasound P-W.…
mi.eng.cam.ac.uk/reports/svr-ftp/hsu_tr534.pdf16 Sep 2005: Pattern Recognition Letters, 24:757–777, 2003. [8] A. H. Gee, N. E. ... Ultrasound in Medicine & Biology, 24(6):855–869, 1998. [18] F. Rousseau, P. -
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mi.eng.cam.ac.uk/reports/svr-ftp/auto-pdf/logan_sst96.pdf9 Aug 2005: #"$%&')(,-.%%&( 0/-#1""324%5/246"78&9:5. ;<8<'7>=@?9A@BA@BDCE<8F@<'8=HGIBDJK=B&L>MNMNO>PQ'/I JRI =BSA@BDCT8=G>=UVI ONJW(XY=ZL@Q. "M>L[AXYU]M>BDU=>_8B?IBDMNM>XaIBD?HQ.BI bDM>XcJ4I Ued=>_#'A@]G[XaI C?@M. ;'&'f2#PI JL[A@L[M>XCMNJKO>XgI,G[MNJABDMNhA@i? @ -
Learning New Articulator Trajectories for a Speech Production…
mi.eng.cam.ac.uk/reports/svr-ftp/auto-pdf/blackburn_icnn95.pdf9 Aug 2005: Figure 1: Variation in error at each of 24 network outputs over the course of the sentence “clear windows”. ... Figure 4 shows the MSE for both the (5,32,24) network trained on the original data and the.
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