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In the result, the selection cutoff is informed by the classifier performance curve: one chooses the maximal acceptable classification error for the problem at hand and stops feature selection procedure as the error on current best subset (green curve in Figures 2 and 3) exceeds this threshold.
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Using results obtained for the Itô integration we investigate the minimal asymptotic errors for the problem of nonlinear Lebesgue integration in the average case setting.
Based on spherical polar coordinates, a quantizer is proposed with a desired relation between the quantized data and the corresponding quantization error for this problem.
The stability theory with explicit error estimate for the problem is still limited.
It is based on Nakao's method by using finite element approximation and its explicit error estimates for the problem.
In contrast, the commonly used viscosity model for VOF introduces a first order error for the same problem.
Sensitivity analysis in addition to this radius together with the FEM computational error for the homogenization problem are carried out here prior to the principal stochastic analysis.
By using the above fixed point formulation, we can define the following p-step projection iterative algorithm with mixed errors for solving the problem (3.4).
This fixed point formulation allows us to construct the following p-step projection iterative algorithm with mixed errors for solving the problem (3.3).
This fixed point formulation enables us to define the following p-step projection iterative algorithm with mixed errors for solving the problem (3.1).
By using this fixed point formulation, we can construct the following p-step projection iterative algorithm with mixed errors for solving the problem (3.1).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com