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In this regard, Tzitzikas et al. formulated finding this mapping as an optimization problem: given n 1=|B 1|, n 2=|B 2|, and n=min(n 1,n 2), the goal is to find the unknown part of bijection M. First, M contains the mapping of all URIs and literals of the knowledge bases (according to Definition 1).
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We assume prediction noise as a Gaussian distribution and formulate finding the optimal hyperparameters as an optimization problem.
It then extracts and synthesizes data from the available evidence base to formulate findings [ 3].
Information that could be triangulated because it appeared in several sources (whether documents or interviews) formed the basis of our findings, although sometimes we had to formulate findings on the basis of single sources of information (in which case these findings are indicated as tentative).
The classical variational inequality problem is formulated as finding a point such that (1.1).
The variational inequality problem is formulated as finding a point such that (1.1).
The classical variational inequality which is denoted by is formulated as finding such that (1.1).
The variational inequality problem is formulated as finding a point x* ∈ C such that.
The problem is formulated as finding a min max solution of a value function.
The problem under consideration in this article is formulated as finding a point satisfying the property: (1.1).
The split feasibility problem is formulated as finding a point x ¯ satisfying x ¯ ∈ K and A x ¯ ∈ D, (1.1).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com