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Two uncertainty measures based on the base classifier predictions and the inconsistency of a predicted label set, respectively, were defined to select the most informative examples.
Two classifiers are built for two newly constructed views for the target data (denoted as f 1 and f 2) where f 1 is initialized to the source prediction function h and f 2 is initialized to 0. The predicted label is calculated as a function of these two views thus creating an ensemble classifier.
The (x) presents true label and (y) presents predicted label.
The column denotes the real target while the row denotes the predicted label.
Then, classification is performed by selecting the maximum value within a dimension as the predicted label. .
When weak classifiers are missing, uncertainty is introduced on each predicted label y j.
Similar(21)
The first measure is Pearson correlation coefficient between predicted labels and manual truth.
The optimization is carried out by successive permutation of the predicted labels.
Finally, the predicted labels for all the test instances are returned (line 25).
From this, final predicted labels are the output thus exploiting unlabeled target data during training.
where (hat {y}_{i}) and y i are single indexed samples of predicted labels and true labels, respectively.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com