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In this paper, we focus on the construction of a computer-friendly Green's function and its convergence for approximate computation.
In a context-sensitive network with n genes and k contexts, however, a (2 n ∙ k) × (2 n ∙ k) [ 23] (or 2 n × 2 n [ 20]) matrix is required for an accurate (or approximate) analysis of the SSD; this results in a computational complexity of O nk22n) (or O nk2 n )) for an accurate (or approximate) computation of the STM.
The problem of approximate computation of probabilities that are known in principle is a recurrent theme throughout the history of probability theory and will be discussed in more detail below.
Mining such frequent co-occurrences can facilitate approximate computation of several useful item similarity measures.
By approximate computation, for the center manifold, we obtain (tilde{alpha}=0) and (tilde{beta}=0).
Clustering-based reweighting method can be taken as an approximate computation way.
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Convergence analysis shows that the representation is uniformly convergent, which is computer friendly, and can be applied to approximate computations.
We employ an approximation framework using Monte Carlo methods and obtain an optimization procedure based on particle representations and approximate computations.
With the proposed morphogenetic neural network it is possible to perform both rigorous and approximate computations (i.e. including semantic uncertainty).
In this paper, a new methodology is proposed that exploits the computation-skipping approximate computing concept.
Kriging based metamodels are popular in approximating computation-intensive simulations of deterministic nature.
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