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Supervised refers to the training step in which the differences between the groups to be classified are learned.
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The concern of employing backpropagation for CSIT training is that we are using supervised learning, where supervised learning refers to the learning of NN in CR LTE-A in which the NN is trained with every possible data set.
Note that the term 'sample' in the context of supervised learning refers to a feature vector derived from a pair of genes and their expression profiles, whereas a sample in an expression data set refers to the gene expression values for a single experiment, e.g. a gene knockout.
Typical semi-supervised learning refers to the use of both labeled and unlabeled data during training.
Cook [19] and Feuz [36] provide a different variation where the definition of supervised or unsupervised refers to the presence or absence of labeled data in the source domain and informed or uninformed refers to the presence or absence of labeled data in the target domain.
Backpropagation refers to a supervised learning method which calculates the error of each neuron after a subset of the data is processed and distributes back the errors through the layers in the network.
The ECDC and EMCDDA Guidance briefly discusses safe consumption rooms (which it refers to as "supervised injecting facilities") as one of multiple aspects of safer injecting behaviour.
This analysis refers to a suite of unsupervised (for example, principal component analysis) or supervised methods (for example, partial least squares discriminant analysis) for analyzing large-scale spectroscopic data sets.
The term refers to the theory and practice of devising and training complex neural networks for supervised and unsupervised tasks.
BP networks are a class of feed-forward neural networks, which refers to the direction of information flow from the input to the output layer, with supervised learning rules.
Teddy Roosevelt refers to texting.
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