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In the unsupervised pre-training phase, a deep belief network (DBN) is pre-trained to obtain initial weights of the subsequent supervised phase.
The pre-formed clusters - the model of the data manifold - in the SOM help prevent the learning of inconsistent labels and thus greatly support accurate learning of class labels in the supervised phase.
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The first stage is a supervised learning phase, based on a statistical approach.
Additionally, a scaled conjugate gradient learning method is employed in the supervised learning phase of the HEFST model.
In a subsequent supervised learning phase, the weights between the SOM hidden layer and the output layer are trained to recognize class labels.
The total length of the active, supervised treatment phase for both groups is planned to be seven days.
The proposed system features two main phases: a radar-supervised training phase and a visual classification phase.
In applying the technique, an initial supervised offline learning phase is followed by a visual behavior data acquisition and an online learning phase during which the cluster head performs an ensemble of model aggregations based on the sensor nodes inputs.
In the supervised back-propagation phase, the network weights are fine-tuned in a supervised manner.
In this phase, supervised data mining techniques such as clustering, classification, and association can be employed for feature selection and predictive modeling.
Our results indicate that REST is required to protect genomic integrity, supervised by S phase surveillance, and that this function is key for regulating proper timing of terminal neuronal differentiation.
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