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Their performance is compared with a metamodelling method using multilinear regression approximation.
The performance of the ANN model was compared with a metamodelling method using a multilinear regression approximation.
The reaction orders of TiCl4, H2, and N2 were extracted by the least-square regression approximation of the experimental data.
To solve the two proposed MINL models, a linear regression approximation is suggested to linearize the models; this technique works based on a piecewise linear transformation.
A simplified model is derived by regression approximation of the mechanistic equations, with which the power draw can be described as an explicit function of the kiln dimension, the operating parameters and the material properties.
When the quality of data is high (dense marker maps, few experimental errors) the global optimization landscapes and the QTL mapping results for interval mapping and the linear regression approximation are very similar.
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Though we have suggested using linear regression for approximation, we may take advantage of other machine learning methods.
The independence of the SNP and CNV was tested by logistic regression with approximation to a general linear model (binomial family) in R, using ANOVA to test the effect of the SNP, followed by the effect of the CNV, then their interaction.
Zhang et al. [32] presents a queueing network model where each queue represents a tier of a web application, which is parameterized by means of a regression-based approximation of the CPU demand of customer transactions.
Instead we approximated relative risks by the odds ratios obtained from logistic regression models, an approximation which is reasonable for a rare disease such as childhood ALL.
Instead, we approximated relative risks by calculating the odds ratios obtained from unconditional logistic regression models, an approximation that is reasonable for a rare disease such as brain tumour.
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