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Quality assurance on collected training data.
For the target we aim to track, we learn features which are most discriminative in appearance matching using the collected training samples by Adaboost algorithm [1, 32].
We used all the training samples in the AFEW training set and collected training samples from the CK+ and MMI DBs, according to the person-independent tenfold cross-validation rule.
A detector is trained from the collected training samples, and a window slides an image from left to right and top to bottom in multi-scales to identify the objects.
A second control was done by training neural networks on the collected training data randomly divided into positive and negative examples (Y-scrambling test).
We collected training and testing data sets from several different resources, and Table 1 summarized these data sets.
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An important issue concerns how and when to collect training data.
Our findings inform strategies for collecting training data and designing and parameterising ensemble classifiers, such as random forests.
A hybrid pixel/object-based change detection approach was developed to effectively collect training samples for historical images with sparse reference data.
Location verification process, or verification process in short, is the set of actions, including data collecting, training, and location inference performed by participating nodes.
It involves facial feature representation, collecting training data, building regression/classification models and model optimization.
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CEO of Professional Science Editing for Scientists @ prosciediting.com