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Although the improvement is not significantly large, this method showed the best performance on both databases.
English subtitles were associated with the best performance on both old and new items.
As we have discussed before, Jelinek-Mercer BPLT+ has the best performance on both CorrectRate1 and CorrectRate3.
As we can see from the experimental results, the proposed algorithm was proven to have the best performance on both jackknife and independent tests based on sensitivity.
As CRFs achieved the best performance on both the discharge summary and article subsets of PhenoCHF, we used only this method to train models in further experiments.
Comparing among these three methods, Jelinek-Mercer BPLT+ obtains the best performance on both CorrectRate1 and CorrectRate3, which are 0.5569 and 0.6167.
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As mentioned in previous sections, the proposed proCMF exhibits the best performance on facial expression recognition in both CK+ and JAFFE datasets.
Briefly, we began by testing 17 feature subsets, each containing one feature and found the subset with the best performance on validation data.
Both Round 1 and Round 2 had the best performance on the test sets when d = 7 (Table 1).
In our sample, volume reduction in both the arcuate fasciculus and the corpus callosum predicted best performance on the Formulated Sentences subtest, with each predictor making a significant and unique contribution.
Experiments are performed on the well-known MNIST database to validate the method and the results show that the system can outperform both SVMs and LeNet5 while providing performances comparable to the best performance on this database.
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Justyna Jupowicz-Kozak
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