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For this task, they require labeled example data that commonly is split in two collections.
This approach can be viewed as a form of "One Shot Learning" [45], [46], where model learning occurs only once, from a single labeled example.
W ij = 1, if both examples from the partially labeled set; W ij = 1, if one partially labeled example and the other from the positively labeled set; W ij = 0, if one partially labeled example and the other a negatively labeled example; It is very natural to multi-task embedding task with our main supervised classification task.
Kajić et al. introduced a modification of an active appearance model for segmenting macular OCT data, which was trained on a large number of manually labeled example images [ 4].
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Most of the existing feature-based methods and graph-based methods require sufficiently large amount of labeled examples or a fully-labeled graph for training.
We used very few labeled examples for initial training.
In many machine learning settings, labeled examples are difficult to collect while unlabeled data are abundant.
Moreover, the number and especially the quality of the manually labeled examples challenge such strategies.
These learning strategies usually require a large set of labeled examples which can be expensive to obtain.
Using the same labeled examples for different scenarios might degrade the system performance.
Acquiring them as labeled examples will probably improve the model's detection capabilities.
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