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We show that models can be trained using a worker's first few submissions to predict their performance in the future.
The significant increase of feature vector dimensionality however introduce noise and thus it does not allow the classification models to be trained using a limited number of samples usually available in clinical studies.
To overcome these drawbacks, we have designed generalized decision trees, which can be trained using a medium-size corpus over groups of similar words to share information on pronunciation, instead of training a separate tree for every single word.
Our contributions are: (i) a CNN architecture that predicts multiple gradings at once, and we propose variants of the architecture including using 3D convolutions; (ii) showing that this architecture can be trained using a multi-task loss function without requiring segmentation level annotation; and (iii) a localization method that clearly shows pathological regions in the disc volumes.
(b) A new object detector can be trained using a McCascade.
Having acoustic and visual streams of feature frames and extracted from pairs of corresponding AV signals s and V (see Section 4.1), a context-dependent AV associator can be trained using a suitable non-linear function approximator: V ̂ ( k ) = h ( S e ( k ) ), where Se ( k ) = E ( S ( k − K / 2 − 1 : k + K / 2 ) ) is an embedded vector obtained from a context of K audio frames around frame k.
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An ANN is trained using a back propagation with Levenberg Marquardt algorithm.
A Support Vector Regression (SVR) model was trained using a diffusion kernel obtained from this graph's Laplacian matrix.
In Experiment 1, chicks were trained using a silver-coloured bead coated with 100% MeA, 0.5% DB or distilled water.
The artificial neural network was trained using a hybrid learning method based on Simulated Annealing and Levenberg Marquardt method.
The neural network model was trained using a carefully selected training signal that contained all the relevant process and controller dynamics.
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