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It estimates the unknown target distribution by selecting relevant samples from both source and target datasets.
In k-NN terminology it is typically distinguished between reference and target datasets.
We propose a novel dual many-to-one encoder architecture to extract generalized features by mapping raw features from source and target datasets to the same feature space.
For two target datasets, the number of time samples in each range cell is 512 (equal to a recorded duration of 0.512 s).
The molecular scaffolds generated as a result of clustering were thus used as SMILES query to search for substructures in both active and inactive target datasets.
Experiments for hetero-lingual text classification were conducted where the source and target datasets contain both heterogeneous feature and label spaces.
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Others are abundant resources for constructing an individualized target dataset.
Build a training and target dataset: Supervised training algorithms require a training and target dataset for the expected outcomes.
It also used as transfer learning when trained on a dataset similar to the target dataset.
The target dataset should be a predetermined set of expected session key values.
The output (target) dataset is created by the measured values of CO2 (ppm).
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