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We apply some weight update rule based on the gradient descent of loss function to optimize the weight of the network and hence, train the network to learn the function required to describe the input examples.
A learning method attempts to automatically construct a classification system from a collection, or training set, of input examples.
These auxiliary examples are then combined with the original input examples to train the local models.
The decision boundary defined by a hyperplane is said to be linear because it is linear in the input examples.
SVMs map input examples to a higher dimensional feature space using a kernel function, and identify a separating hyperplane that maximizes the margin or distance from the hyperplane to the nearest positive and negative examples [ 30].
These concerns are (1) the size of the dataset, (2) the need for dealing with input examples of variable length, (3) the need to have probabilistic outcomes, and (4) the need to perform multiclass classification.
In order to calculate the weight vector for a specific HPO term j (W j), we first identified the subset of input examples (i.e. proteins) that are annotated with the given term (referred to as S j).
We concatenate four frames as a single-input example.
For an input example x, this estimation consists in applying the first three levels of every detector to x.
The input example x first goes through the three levels of detectors H-30, H0, and H+30.
Figure 1 depicts the resulting data structure for a small input example.
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