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The sample is updated every year using information from the variables selected from the Social Security system dating back to when computerized records began, and from other administrative data sources which record additional information on individuals.
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Here, the sampling is updated to be consistent with the latest family-wide monograph [ 28, 80], in which 183 genera are accepted (see Additional file 1 for the list of genera used).
The weights of these samples are updated by applying the selected weak classifiers using the same process in AdaBoost.
After a weak classifier is selected, the weights of the samples are updated by applying it as in (3).
In the proposed system, this is handled as follows: By applying weak classifiers to another set of samples, the reservoir set, the weights of these samples are updated with the same process that updates the weights of the samples in training.
The weights of the samples are updated by (13) where, and α is a positive parameter.
The weights of samples are updated according to the classification result of the new instance.
If the seizure cannot be predicted (i.e., the seizure alarm missed), the preictal sample pool Spre is updated by using the samples of 30 to 40 minutes immediately preceding the seizure onset.
Then, before sampling α i, the right-hand-side for α i,, is obtained efficiently as: 29 and after sampling α i, is updated as: 30 where and are the values of α i before and after sampling.
The signal is sampled and control is updated only when a predefined condition gets violated while ensuring acceptable closed-loop behavior of the system.
In the time-stepping process of the proposed method, the column vector containing the spatially sampled field values is updated by multiplying it with an iteration matrix, which is written in a matrix-exponential form.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com