Vertebrates (phylum Vertebrata) are separable into at least seven discrete classes that represent evolutionary groupings of related animals with common features.
The polydisperse PM2.5 class poorly represented the behavior of discrete classes.
The scale of probability and impact has five discrete classes between very low and very high.
Bivariate mapping consists of classifying map objects into discrete classes based on the values of two variables.
The classification algorithm mainly aims at categorizing the whole image blocks (i.e., range and domain blocks) into three discrete classes.
The necessity of establishing boundaries for phenotypic classes arises because, although pigment intensity is a continuous variable, genotypes are, by their nature, discrete classes.
Four discrete classes, which correspond to π(P) being 0, 1, π(E+) or π(E−), and five essential classes which we describe below.
The discrete classes are, respectively, the finite rank projections, finite co-rank projections, the Sato Grassmannian of H+ and the Sato Grassmannian of H−.
Supervised and unsupervised classifications rely on classical set theory in assigning pixels into discrete classes based on training sets and some statistically determined criteria.
Using latent class analysis we identify three discrete classes of people based on their HLC: the first class is sceptical about their capacity to control their health and certain unhealthy habits.
While QuickBird/IKONOS data are classified into discrete classes, at Landsat and MODIS resolutions, sub-pixel cover is delineated using non-parametric ensemble regression trees from the random forest family.
