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By introducing a PDE-constrained optimal control framework, it is possible to use the training data resulting from multiple ways (ground truth, results from other methods, and manual results from humans) to learn PDEs for different computer vision tasks.
Then we use the training data in order to obtain support vectors which define the discriminative classifier.
Since it is not possible to know a priori which scale is more discriminative, we propose considering the different Gabor scales as different streams of information and use the training data to learn multi-stream CHMMs (mixture and state level).
We thus use the training data in an optimization procedure to find the correct values for w.
We use the training data sets to build the models and validate the selected models using the validation data sets.
Local genetic values (GEVs): Use the training data to obtain a model to estimate the local GEVs g j ∗ ^ (m ) for each genome region j = 1, 2, …, k.
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Various neural network architectures were trained using the training data set, and the accuracy of the model obtained was examined by using the test data set.
Three different ConvNet architectures were evaluated using the training data: 1) a simple 3-layer ConvNet architecture, 2) a typical 4-layer ConvNet architecture, and 3) a deeper 6-layer ConvNet architecture.
Train LSSVM through using the training data.
The Spanish recognizer was trained using the training data provided by the organizers.
The parameters of the Vanilla benchmark model are first estimated by using the training data.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com