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Here we propose a conceptual and computational framework where policies are formulated as binary trees, using a simulation-optimization approach.
The prediction procedure is usually formulated as binary classification problem.
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The problems are formulated as Binary-Mix-Integer-Linear-Programs (BMILP), which are solvable by any standard branch-and-bound routine.
These were formulated as concretely as possible.
This problem is formulated as a binary labeling problem based on a Markov random field (MRF) framework, the solution of which is a binary map for each exposure image, which identifies the pixels to be excluded in the fusion process.
This problem can be formulated as a binary integer programming problem, which can be solved easily.
The robust signal timing problem is formulated as a binary integer program.
The problem is formulated as a Binary Mixed Integer Linear Program (BMILP) which is solved by standard branch-and-bound method.
Watermark detection can be formulated as a binary statistical decision problem, so, its performance is dependent on the accuracy of statistical modeling.
This problem has been formulated as a Binary Mixed Integer Linear Program (BMILP) that can be solved by a standard branch-and-bound routine.
The optimization is formulated as a Binary Mixed Integer Linear Program (BMILP) which is solved by standard branch-and-bound routine.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com