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Here, we introduced how to extract the nonshadowed pixels by using the trained Gaussian darkening factor model. Figure 18 gave us the rules to calculate the difference between the mean of Gaussian model and the darkening factor, and check if the difference was smaller than 3 times of standard deviation.

The probability of classification can be interpreted as a measure of meditation ability by using the trained classifier to predict class membership in novice meditators.

For each GO category, 20 000 pairs of probe sets were selected randomly and 5000 pairs of them were used for training of a SVM and the remaining pairs were used to evaluate the performance by using the trained SVM.

After the cross-validation of training set, the independent test set was evaluated by using the trained model with the highest accuracy.

By using the trained model of the platform, we are able to test against micro-expression recognition.

In a reverse process, genetic algorithm is employed to search for a near-optimal design which satisfies the designer's required product image by using the trained neural network as a fitness function.

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The experiments are performed on a PC with 3.2 GHz CPU and 6 GB RAM by using the training sequences used for the threshold modeling and the non-training sequences as seen in Table  2.

Then the algorithm was updating the parameters iteratively until convergence, by using the training set Tr.

The parameters of the Vanilla benchmark model are first estimated by using the training data.

The goal of the training phase is to induce a classifier from inp → lab by using the training data X.

After the learning process of ANN by using the training set, the model should be tested in unseen data, a test set and sometimes a validation set.

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