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MORTGAGE DEFAULT: CLASSIFICATION TREES ANALYSIS David Feldman* and…
mi.eng.cam.ac.uk/~mjfg/local/4F10/Feldman_Gross.pdf15 Nov 2005: and economics, both theoretically and empirically. Amongst these aspects, mortgage. default has been one of the leading topics. -
Hand PoseEstimation Using Hierar chical Detection B. Stenger��� , ...
mi.eng.cam.ac.uk/reports/svr-ftp/stenger_hci04.pdf8 Aug 2005: exam-ple,by AthitsosandSclaroff for handposeestimation[1] andShakhnarovich et al. -
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mi.eng.cam.ac.uk/reports/svr-ftp/liao_interspeech05.pdf26 Sep 2005: SPLICE [1] is one recent exam-ple of this approach. Alternatively in model-based approaches,the parameters of the system are altered to reflect speech in thenew acoustic environment. -
SVMS, SCORE-SPACES AND MAXIMUM MARGIN STATISTICAL MODELS M.J.F. Gales …
mi.eng.cam.ac.uk/~mjfg/BeyondHMM.pdf15 Jun 2005: This is best illustrated by exam-ining maximum margin training of a univariate Gaussian class-conditional distribution. -
Stereo Coupled Active Contours Tat-Jen Cham Roberto Cipolla…
mi.eng.cam.ac.uk/reports/svr-ftp/auto-pdf/cham-cipollaCVPR97.pdf9 Aug 2005: Table 1 showhow different tracking modes can be effected. For exam-ple if a rigid curve is to be tracked under variable epipolargeometry (eg. -
Contour-Based Learning for Object Detection Jamie ShottonDepartment…
mi.eng.cam.ac.uk/reports/svr-ftp/shotton_iccv05.pdf8 Aug 2005: Training examplesEach training image can generate multiple training exam-ples. Each example is a vector of feature responses takenat a particular centroid and is given a binary target value,positive, meaning ... After each round, all training exam-ples -
TWO-WAY CLUSTER VOTING TO IMPROVE SPEAKER DIARISATION PERFORMANCE S.…
mi.eng.cam.ac.uk/reports/svr-ftp/tranter_icassp05.pdf25 Mar 2005: This reduces the num-ber of all possibleclusterings of the base-segments for this exam-ple from Bell(9) = 21, 147 to 2 Bell(3) = 10. -
A Probabilistic Framework for Perceptual Grouping of Features for ...
mi.eng.cam.ac.uk/reports/svr-ftp/auto-pdf/yow_fg96_1.pdf9 Aug 2005: For efficiency, the geometric feature vector & is exam-ined first. If the feature vector fails to be a valid instance of. -
Face Recognition with Image Sets Using Manifold Density Divergence ...
mi.eng.cam.ac.uk/reports/svr-ftp/oa214_CVPR_2005_paper1.pdf8 Aug 2005: asfaces with grossly incorrect scales – see Figure 9 for exam-ples of successfully removed false positives. -
Article Submitted to Computer Speech and Language Automatic…
mi.eng.cam.ac.uk/reports/svr-ftp/auto-pdf/kim_csl04.pdf9 Aug 2005: of capitalisation generation are exam-ined for reference word sequences and reference punctuation marks.
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