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Model-transferring domain adaptation methods use a previously trained source classifier for adaptation purposes on a target domain.
These methods require trained source classifiers and the target data during training thus the source training samples are not required for building the classifier in the target.
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The research staff will be trained in source data verification and will be supported in preparing standardized reports.
The outcome variable is "participation in on-the-job training" Source: SOEP v26, own calculations.
In many real classification scenarios the distribution of test (target) domain is different from the training (source) domain.
Machine learning algorithms often suffer from good generalization in testing domains especially when the training (source) and test (target) domains do not have similar distributions.
However, due to the lack of the training source of the packet SNR measurement, the measurement-based model in [31] can not be established in our protocol evaluation.
Nevertheless, it was demonstrated in [15 20] that the accuracy of a generic (pedestrian or vehicle) detector would drop-off quickly when it was applied to a specific traffic scene, in which the available data would mismatch the training source one.
We then employed Clementine 10.0 software on the RNTech samples to assess whether rules set based on apoB-100-normalized C-I and C-III, CA19-9 and CRP serum levels can be used to classify between sera of control and stomach cancer groups of RNTech source as a training source.
Table 4 presents data on the NMC register, broken down by SHA of resident postcode, by training source (foreign or UK).
Even with only 10% of known interactions as a training source, the supervised methods outperform the unsupervised methods by a wide margin.
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