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The high-level concept of transfer learning is to improve a target learner by using data from a related source domain.
Therefore, there is a need to create a high-performance learner for a target domain trained from a related source domain.
However, by enhancing the training with supplementary labeled data from a related source domain, the model's ability to classify target instances can be improved.
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However, outputs from our two main models and accompanying sensitivity analyses strongly suggest that bears could not have persisted in situ, and are likely to have recolonized Ireland during the Woodgrange Interstadial from a closely related source population (probably on unglaciated southern Britain).
The paper by Eaton [31] proposes to build a target learner based on a transferability measure from multiple related source domains.
Transfer learning (TL) [3, 6, 7] aims to produce an effective model for a target task with limited or no labeled training data by leveraging and exploiting knowledge from a different, but related source domain to predict the truth label for an unseen target instance.
Assume that the initial problem data consists of a corpus of n = nT + nS documents, where nT is the (small) number of labeled documents available for the target domain of interest and nS >> nT is the number of labeled documents from some related source domain; in addition, suppose that a modest lexicon Vl of sentiment-laden words is known.
The genetic data suggest that the mice of both primary invasions originated from related source populations.
Still, it remains noteworthy, and also unexpected, that two separate primary invasions on the Kerguelen have come from related source populations.
One possible explanation of our data is that two menA genes were present in the prokaryotic endosymbiont, one of cyanobacterial provenance and other introduced by HGT from another distantly related source.
F. nucleatum strains isolated from amniotic fluids and placentas along with those isolated from orally related sources invaded both epithelial and endothelial cells.
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