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For example, Liu et al. [11] use the notation of supervised transfer learning to denote a fully-labeled source domain and a limited labeled target, while unsupervised transfer learning denotes a mostly labeled source with no target domain labels.
The generation of sufficient labeled target for this experiment required multiple labeling reactions, and pooling all the labeled targets prior to distribution over the arrays normalized potential variation between these reactions.
An additional feature to consider when labeling target material is whether to use end-labeling or internal labeling strategies.
The boosting process requires some amount of labeled target data.
Magnetite cationic liposomes (MCLs) were used to label target cells.
Finally, the target learner is built from the labeled and pseudo labeled target data.
For this scenario, there is an abundance of labeled source data and limited labeled target data.
Also the terms "class", "activity", "label", "target" and "target protein" are employed interchangeably.
A learner is now trained with the transformed labeled target instances.
Uniformed Feature-Space Remapping (UFSR) is used when no labeled target training data is provided.
A baseline method is tested using an SVM classifier trained only with the labeled target data.
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