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The difference can be explained by at least three factors: a different training set, different settings of the training parameters for the Viola and Jones classifier, and different ground truth data.
In the second experiment we investigated the imbalance problem to determine the optimal settings of the training set for each classifier in varying "real-life" conditions.
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Using these settings, the structures of the training set were mapped to hashed fingerprints with the default settings.
The extremely large-scale gait database enabled us to evaluate recognition performance under cooperative and uncooperative settings, the impact of the training data size, the recognition difficulty level of the CS labels, and the possibility of the classification of CS labels.
In the Netherlands, there are 35 hospital settings where the training of paediatric SpRs takes place.
Although the present tests constitute basic research, performed on novices without experience of visualizing medical images, our ultimate goal is to apply this method of medical image display in clinical settings, particularly in the training of novice medical personnel.
The work also discusses the influence of vigilance parameter settings for filtering the training set.
Studies from other settings demonstrate that the training of general doctors with surgical skills and nurse anesthetists is possible, even in a conflict zone [ 16, 17].
The three models were tested in a series of experimental settings which varied in the size of the training set, the number of basis compounds, and the coefficients used in defining similarity between compounds.
Using the same settings of training and test samples, the bias-corrected ME (MEBC) shows significant reductions in MSD1 from 4.57 to 1.10 m, and MSD2 from −4.39 to −1.13 m (Table 1).
Different settings of SVM training parameters were tested, and the best classifier was constructed with γ = 1.2 and C = 1.2.
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