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thesis.dvi
mi.eng.cam.ac.uk/reports/svr-ftp/nock_thesis.pdf14 Jun 2006: different points during my postgraduate career. Results that match 2 of 3 words
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Journal of VLSI Signal Processing manuscript No.(will be inserted ...
mi.eng.cam.ac.uk/~mjfg/layton_JVLSI07.pdf22 Nov 2006: Although, for many tasks, enu-merating all possible sub-strings yields a very high di-mensional feature-space, this feature-space is sparse withnon-zero entries being calculated using efficient dynamic. ... Viterbi de-coding yields a single sequence (the -
Sparse and Semi-supervised Visual Mapping with the S3GP Oliver ...
mi.eng.cam.ac.uk/reports/svr-ftp/williams_cvpr06.pdf3 Apr 2006: the proportional decrease inthis requirement means that gaze tracking at 10–15Hz con-stitutes a “background task” leaving the majority of cyclesfree for other processes. ... If this paper is accepted forpresentation at CVPR, text entry with S3GP -
TextonBoost: Joint Appearance, Shape andContext Modeling for…
mi.eng.cam.ac.uk/reports/svr-ftp/shotton_eccv06.pdf15 Feb 2006: The potential takes the form of alook-up table with an entry for each class and pixel location,. ... Sub-sampling and random feature selection for training efficiency. Theconsiderable memory and processing requirements make training on a per-pixelbasis -
MODEL-BASED TECHNIQUES FORNOISE ROBUST SPEECH RECOGNITION Mark John…
mi.eng.cam.ac.uk/~mjfg/thesis.pdf5 Jun 2006: MODEL-BASED TECHNIQUES FORNOISE ROBUST SPEECH RECOGNITION. Mark John Francis Gales. Gonville and Caius College. September 1995. Dissertation submitted to the University of Cambridgefor the degree of Doctor of Philosophy. Declaration. This
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