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In 2013, five new distinct epithelial tumors and three provisional entities have been added to this classification, relying on morphology, immunohistochemistry, cytogenetics, and molecular pathology advances.
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Most Machine Learning (ML) methods, from clustering to classification, rely on a distance function to describe relationships between datapoints.
The classification relies on the analysis of the eigenvalues of the transfer matrix for a unit-cell and the asymptotics derived for the transmission coefficient.
The classification relies on the motion pattern attributes associated with the trajectories of each road-user type, namely, vehicles, pedestrians and cyclists.
In principle, we would imagine that the classification relies on the visual content surrounding the keypoints.
Both biological classification and folkbiological classification rely on direct contact and experience with plants and animals in the natural environment (Medin and Atran 1999).
First, the results of behavior classification rely on the RT thresholds and are not meaningful unless the identified thresholds are validated.
However, this requires the objects to be detected to possess a sufficient amount of well separating features as classification relies on many cascaded stages containing several image features.
Since the Charnley classification relies on an assessment of the severity of the associated conditions, it might not be suitable in studies based on chart reviews or extraction of medical records.
The target classification relies on two steps: at first the target is identify following the same process as Section 4.1.1 and then classified into two classes minelike and nonmine.
This kind of classification relies on the traditional layered design of networking: the PHY and MAC layers ensure a perfect connection of some preselected links and the routing layer selects the path from these links.
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