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13 Mar 2018: We perform the adapted online feature selection algorithm (see Alg. 1) on a numberof test sequences. ... We take a single test image, and create a test sequence by adding fixed size, randomlypositioned black squares to simulate occlusion.

20 Feb 2018: class-based trigram 4.8 3.4. Table 1. Test results for the speech recognizer (%WER). ... Forthe NL test, the semantic parser used as input the reference tran-scriptions instead of the recognized output.

13 Mar 2018: We used 18randomly selected training/test combinations for reporting identification rates. Comparative Methods. ... 0.9. 1. Dimension. Iden. tific. atio. n ra. te. Effect of the dimension on the test set.

20 Feb 2018: For the test material 30 sentences from a tourist information en-quiry application were used. ... JVF IVF. Fig. 2. Comparison between CF-IVF and CF-JVF on a forced choicepreference test.

20 Feb 2018: Figure 6: Results of the ABX test. between each source and target speaker pair. ... perc. enta. ge. PSHM. JEAS. MM MF FM FF. PSHMJEAS. Figure 7: Results of the quality comparison test.

13 Mar 2018: 418. 2. DATA ACQUISITION SETUP In order to develop the tests here exposed, a set of 3D facial data have been acquired. ... The processing time was shorter than ten seconds even for the worst situation met during test step.

13 Mar 2018: The parameters for both methods are setby testing the classification performance on a test setof 5000 images. ... In a first approach,the edge and colour cost terms are computed for a numberof test images.

13 Mar 2018: State-of-the-art commercial system FaceIt by Identix[12] (the best performing software in the most recentFace Recognition Vendor Test [13]),. • ... KLD) [14]. In all tests, both training data for each person in the gallery,as well as test data,

13 Mar 2018: The model can now be used to classify test imagepatches as text or non-text. ... The test patches were ex-tracted in the same manner as the training patches.

13 Mar 2018: Iden. tific. atio. n ra. te. Effect of the dimension on the test set. ... Anindependent illumination set with both training and test sets was exploited for the val-idation.

3 May 2018: Test subjectswere asked to assess the quality of the speech on a 1-5 scale. ... 10 test subjectswere used. Models A and B are compared and models A and C arecompared.

13 Mar 2018: M}. For a test image xt, the task is to predictyt A, i.e. ... The vali-dation set and the test set contain 2104 and 2967 imagesrespectively.

13 Mar 2018: We capture six test scenes with the same five objects. Eachtest scene has 400 500 frames containing multiple objects with different back-grounds/clutter and poses.Scenario 4: This scenario tests ... Recall. Pre. cis. ion. LineModSupp. SIterative(Edge).

13 Mar 2018: Quantitative test was done on unsegmented KTH datasetusing the classifiers learnt in the previous experiment. ... In test set-up, we used unsegmentedKTH data for incremental training (i.e.

13 Mar 2018: This is achieved by sampling the network with randomly droppedout units at test time. ... Table 3: Pascal VOC12 [9] test results evaluated from the online evaluation server.

13 Mar 2018: 0. 0.1. 0.2. 0.3. 0.4. 0.5. Test index. Rel. ativ. e re. ... The tests are shown in the order of increasing raw data performance foreasier visualization.

20 Feb 2018: Table 2 shows the results on the test sets. Consequently, when evaluating on the DSTC2 test set, awindow of 4 (w4), performs slightly better than other window sizes and better than ... On the In-car testset, a context window of 4 outperforms all the

13 Mar 2018: cluttered background, and background with skin colour). The overallaccuracyon 1025 test cases is 89.7%. ... Thepercentage of test cases that cannot be mapped into any classis 20.3%.

13 Mar 2018: We used 18 randomlyselected training/test combinations of the sequences forreporting identification rates. ... The test recognition rates changed byless than 1 percent for all of the different trials of randompartitioning.

13 Mar 2018: High dimensional inputspace and a small training set often cause over-fitting of classifiers to the training data and poorgeneralization to new test data. ... 3. Nevertheless, the twointersection sets of the train and test sets are stillplaced in the