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8 Aug 2005: 4. Includ ing EdgeOrientation Impr ovesClassificationPerformance.Thisexampleshowsthe classificationresultson a test setusing a marginalized template.

9 Aug 2005: The global error covariance matrix foreach network mapping is estimated fromits performance on an unseen test set, andthe Jacobian matrix is found by extendingthe usual error back-propagation formulaeto evaluate the

12 Jul 2005: Foreach individual in the database we collected a training and a test video sequenceof the person’s face in random motion, sampled at 10fps. ... Illumination conditions were mildly different in training and test sequences,see Figures 8 and 9.

10 Jul 2005: Training of allalgorithms was performed with data acquired in a single illumination setting and testingwith a single other – we used 9 randomly selected training/test combinations. ... time 7.8 11.8 11.8 0.8 45 7.0 7.0. Table 2:Evaluation results:The

9 Aug 2005: cast News Benchmark Test Evaluation, the general procedures used are described and previous. ... Test Evaluation, four sites (BBN, MITRE, SPRACH and SRI) participated and submitted their.

9 Aug 2005: The word error rate resultsare based on the six shows of the 1996 Hub 4 development test,and were generated by rescoring lattices produced by a simplifiedversion of the 1996 Hub

9 Aug 2005: 62. 6.6 Conclusions. 63. 7 Conclusions 69. 7.1 Acknowledgements. 70. A Significance tests 71. ... A.1 McNemars Test. 71. A.2 Critical ratio for the difference in two s.

9 Aug 2005: A techniquethat optimises the length of individual n-grams is proposed, and experimental tests show it to lead toimproved results.

9 Aug 2005: Training- and test-sets werecreated by splitting the material evenly acro ss these topics in the. ... A 65K vocabulary was used to build language models. Thestandard 2.1 million word set-aside dev-test text for WSJ0 was used as a test-set.

26 Sep 2005: to test it. This was recognised early on, thus most noise robustness methods can be classified under several. ... Test Conditions. Figure 3.1: Methods of reducing the acoustic mismatch. 3.1 Inherently Robust Front-Ends.