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For this purpose, the drilling mud is chosen according to the nature of training, the architecture of the well, the economic objectives and the environmental constraints.
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The second, which uses real coding, takes its inspiration from mathematical morphology theory and trains the architectures output by the binary genetic algorithm.
The efficiency of training the proposed architecture using stochastic diffusion search algorithms is evaluated.
Inter-subject methodology refers to training the proposed architecture on multiple subjects and testing it on a brand new subject.
Focus is on how to select the most highly informative samples for effectively training the neural architecture.
We used about 70% of this data for training the deep neural architectures.
The first step is to train the proposed architecture under two reasonable assumptions.
We first train the proposed architecture on 12 video recordings acquired in controlled scenarios and test it on the remaining 24 samples acquired in the same conditions, and we repeat this test for all combinations of such video recordings pertaining to the subject analyzed.
Hence, we train the same architecture of [2] as in vessel classification task and modify it accordingly with an aim to capture more details in vessel images: For the last layer, rather than 26 label classes, we use 109, 4035, and 4144 label classes.
The entire model is then globally constructed by jointly training the convolutional and DNN architectures as a whole using back-propagation (see Fig. 1 b for an overview).
In the sample-identification phase, the best versus second-best (BvSB) rule is applied to determine the most useful samples; the labeled samples as-selected and all unlabeled samples are then combined to train the BvSB-ADN architecture.
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