However, traditional satellite-based and raster classifications rely on pixels that are assigned to a single category and often generalized.
Supervised and unsupervised classifications rely on classical set theory in assigning pixels into discrete classes based on training sets and some statistically determined criteria.
The functional annotation and classification relied on the SEED subsystem [ 27].
The functional annotation and classification relied on the SEED subsystem ([ 69]; http://www.theseed.org/wiki/ Home_of_the_SEED) databases.
MITE classification relied on homology with longer MLEs (in the TIRs and internal sequences) and on the breakpoints of deletion/rearrangements.
When no such lesion was visible on imaging, classification relied on established criteria of unilateral motor or sensory signs, or both, involving at least two out of three body parts (face, arm, and leg) without disturbance of cortical functions or consciousness (14).
In principle, we would imagine that the classification relies on the visual content surrounding the keypoints.
Most Machine Learning (ML) methods, from clustering to classification, rely on a distance function to describe relationships between datapoints.
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.
