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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.
While highly effective, these methods require trained surgical providers and medical centers that have the necessary space and equipment.
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These methods require training the underlying statistical models on data representative of diseased and healthy phenotypes in order to make such predictions.
All the above, QTA-based methods require training data, while Singhal and Seborg [33] proposed a pattern-matching-strategy requires no training data but a huge amount of historical data.
This per-pixel image classification methods require training samples, which can be gained from a field visit to selected spots or via visual interpretation of aerial photographs or from Google Earth.
Some of these limiting factors include time (roads need to be closed off during inspections causing transportation disruption), site work on highways can be hazardous for inspectors, manual collection methods which often require trained and experienced inspectors, and the use of destructive techniques.
Despite efforts to automate retinal image evaluation (11– 13), currently no system can perform such analyses in real time, and present methods of retinal imaging require trained imagers to acquire retinal images.
However, blood sampling is an invasive method that requires trained personnel and can cause considerable stress to the animals.
Other nucleic acid-based methods were expensive and required trained personnel.
Current PEC defect classification methods require highly trained personnel and the results are usually influenced by human subjectivity.
However, the accuracies of these methods can be affected by the detection performance in the face region and these methods require many training samples [31].
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