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An approximate entropy (ApEn) was extracted from the approximation and the detail coefficients.
To this end, two new approximate expressions to estimate the SNR are derived accounting for the approximation and quantization errors, respectively.
The solution technique approximates mean conditions, and solves the deterministic model to refine the approximation, and eventually converge on a solution.
Thus, we decouple the approximation and cost-recovery objectives.
The proposed risk-sensitive loss functions minimize both the approximation and estimation error.
We predict the error behavior of the approximation and demonstrate this on an application.
It follows that every dual of J T) has the approximation and π properties.
Since the interactive activation model fit the data pretty well, Newell might have advocated accepting the approximation, and moving on to other issues.
For better reconstruction performance, we proposed a balance coefficient between the approximation and regularisation terms and a method for optimisation.
In this paper we present a novel architecture and algorithms for the approximation and inversion of many-to-one functions.
We give explicit expressions for both the index and the approximation and discuss some properties of the index.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com