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Consequently, this ordered class of channels is a subset of the class for which we establish the common message secrecy capacity.
On the other hand, adding an element at the "end" of such a well ordered class will give an ordering that is not similar: $1 , 2 3, \ldots \text{etc.}, 0$.
The COR expresses the ratio of odds for having a DD sum score greater or equal than that in any ordered class i, compared with that in all lower classes.
Get on their website and search for "Court Ordered Class".
Definition 1 The mean S ∗ is a right cancelling mean for an ordered class of means Δ ⊂ Ω if there exists M ∈ Δ, M ≠ S ∗ such that S ∗ ( a, b ) ≥ M ( a, b ), but there is no mean N ∈ Δ, N ≠ S ∗ such that the inequality N ( a, b ) ≥ S ∗ ( a, b ) holds for each a, b ∈ R +. Definition of the left cancelling mean S ∗ is analogous.
First, we denote a set of training data (x i,y i )∈R d ×R (i=1,2,⋯,M; M being the number of training samples) as T M. Each x i ∈R d is a d-dimensional (d being the number of features shown in the previous subsection) input feature vector, and y i ∈{1,2,⋯,K} is the corresponding ordered class label, where K is the number of classes.
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In the proposed method, we perform ordinal regression with K ordered classes.
It assumes ordered classes and extends the range of predictions considered correct to the n neighboring class values.
Restriction and sequence analysis of captured clones revealed five highly ordered classes of cAAV, each of which contained a defined segment of the integrated vector locus.
Similarly, numbers may be reduced to collections of classes; points and instants may be reduced to ordered classes of volumes and events; and classes themselves may be reduced to propositional functions.
The sum $1 + \omega$ will be the relation number of ordered classes which result from adding one element at the beginning of the ordering, say $0 , 1 2, 3, \ldots$ etc., which has the same ordinal number $\omega$.
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