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Furthermore, since the intensity of facial AU is a non-negative value (i.e., the intensity values are between 0 and 5), we impose a non-negative constraint on the estimated intensities by restricting the search space for the dictionary learning and the regression function.
A non-negative value λ is a hyper-parameter.
This process continues until a non-negative value is read from "Arrow Memory".
Equations (28) and (29) indicate that input and output weights should be a non-negative value.
A similarity function is an algorithm that compares two feature vectors and returns a non-negative value.
Once a non-negative value from "Arrow Memory" is read (i.e., ), the system enters the next state which is the "Read Queue" state.
Similar(45)
If Φ is a non-negative valued function and A 1 = ∫ R n Φ ( y ) | y | n | y | α + n q − λ d y < ∞, then H Φ is a bounded operator on M K ˙ p, q α, λ ( R n ).
The dissimilarity measure can be formalized by a function defined on pairs of descriptors indicating the degree of their resemblance; a dissimilarity measure d on a set S is a non-negative valued function ({d}:{S}times {S}mapsto mathbb {R}^cup {0}).
(But in a partitional hierarchical clustering (Chapter 17 ) all members of a cluster are of course also members of its parent). On the definition of hard clustering that permits multiple membership, the difference between soft clustering and hard clustering is that membership values in hard clustering are either 0 or 1, whereas they can take on any non-negative value in soft clustering.
The parameter η m is the distribution index for mutation and takes any non-negative value.
Here, the parameter η c is the distribution index for SBX and can take any non-negative value.
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