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Incremental feature search.
A scalar DatasetMeasure is computed multiple times on variations of a certain dataset. These measures are in turn used to incrementally select important features. Starting with an empty feature set the dataset measure is first computed for each single feature. A number of features is selected based on the resulting data measure map (using an ElementSelector).
Next the dataset measure is computed again using each feature in addition to the already selected feature set. Again the ElementSelector is used to select more features.
For each feature selection the transfer error on some testdatset is computed. This procedure is repeated until a given StoppingCriterion is reached.
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errors = StateVariable()
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Proceed and select the features recursively eliminating less important ones. Returns a tuple with the dataset containing the feature subset of
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