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The surface fit method uses least squares to solve the problem of finding the best surface fit to the data, whereas the region grow method uses the surface slope and a set of properties (e.g. maximum slope between adjacent pixels) to generate surfaces from single seed GPS-positions.
The Tk-GV method uses least relative error of CL as a minimization target (see the Appendix) and is more robust than GV fitting with least-squares regression.
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We propose an analytical method using least cost analysis in a Geographic Information System (GIS) to empirically measure social integration.
This study also develops a financial analysis method using least-cost and benefit-cost models to assess the financial profitability of implementing LM program from a participant's point of view.
For example, traditional LSSVR localization method uses the least squares method to eliminate the influence of measurement errors and can obtain a better localization effect.
The method uses a least squares based plane fitting procedure and this results in a lesser number of design variables for optimization than existing approaches.
The proposed method uses the least design variable, i.e. only the number of nodes needed to find a configuration of an irregular tensegrity structure, without any information on the symmetrical geometry or other predefined initial structural conditions.
The method uses Generalized Least Squares (GLS) to model the autocorrelation of the data and estimate regression parameters to produce an effect size that represents the magnitude of treatment effect from baseline to treatment phases in standard deviation units.
The present method uses moving least-squares (MLS) interpolation to construct shape functions based on a set of nodes arbitrarily distributed in the analysis domain.
In order to identify which instance this is, three methods were used: least confident sampling, margin sampling and entropy-based sampling.
The methods they used least were palliative and emotive coping.
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