Exact(4)
As a byproduct of state dimension reduction, we also show how to identify low-dimensional subspaces of the data in problems with high-dimensional observations.
Granular data and granular models offer an interesting tool for representing data in problems involving uncertainty, inaccuracy, variability and subjectivity have to be taken into account.
On the other hand, Lowrie and Kay (2011) observed that when students apply visual strategies to understand the data in problems that they found complex, they tend to use the analytical strategies in easier problem types.
Moreover, the data in problems of integral geometry are of quasianalytic character, i.e., their values specified in a domain of the Lebesgue measure can be as small as desired, determine their values in an essentially larger domain (see [[32], Chapter 6, Section 17] and [[33], Chapter 6, Section 1]).
Similar(56)
The dominant location of uncertainty is associated with data in problem formulation, exposure assessment and effects assessment.
We believe that at least at present the data lock-in problem may be the most overlooked.
This practical orientation, together with the introduction of contemporary data in commercial problems, suggests that mathematics had begun to reach beyond the confines of mandarin society.
This paper proposes a multi-objective fuzzy mathematical programming model for designing an environmental supply chain under inherent uncertainty of input data in such problem.
Later, the usefulness of other types of genomic and proteomic data in this problem is also proved.
However, the data in real-world problems are often imprecise or ambiguous.
However, the observed values of the input and output data in real-world problems are sometimes imprecise or vague.
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