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In the proposed optimization method, the weights W i,s, W i−j,s also directly affect the optimization results.
In this inversion method, the weights of three constraint conditions, namely, spatial smoothing, initial value damping, and the boundary condition are optimized by minimizing Akaike's Bayesian Information Criterion ABICC) (Akaike, 1977, 1980).
In the subjective method, the weights of indicators were determined by expert scoring and AHP to avoid unreasonable weight assignment subjectively.
There are three main parameters involved in our method: the weights of the disease-protein connections, the weights of the protein-complex connections, and the restart probability in the random walk model.
32 33 Using this method, the weights are the inverse probability of assigning a treatment derived from a logistic model with warfarin treatment as the dependent variable and all observed patient level and hospital level characteristics as the independent variables.
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In the weighted average method, the weight is assigned to each expert.
In collocation method, the weighting functions are obtained from the family of Dirac δ functions in the domain.
In order to apply the improved method, the weight between node 2 and node 3 is assumed to be infinite.
The results reveal that with the proposed optimization method, the weight of composite structures could be further reduced.
Finally, via the proposed weight value iterative method, the weight values of the training sample are converged, and the model with good predicting performance is obtained.
There is a parameter in the proposed method: the weight ω c of the within-classes scatter matrix of the center region in (2).
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