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Particularly, annealing and Monte-Carlo techniques are used to minimize an error between correlation functions of model and sample.
In this formulation, we propose to minimize an error function which combines the deviations of the local fields from the corresponding macroscopic ones.
The net was trained using the back propagation algorithm, which uses a gradient search technique and iteratively adjusts the weight coefficients in the network to minimize an error function equal to the mean square difference between the desired and the actual net output [14].
The number of information granules produced for each context is adjusted so that we satisfy a certain reconstructability criterion that helps us minimize an error between the original data and the ones resulting from their reconstruction involving prototypes of the clusters and the corresponding membership values.
To construct an optimal hyperplane, SVMs employ an iterative training algorithm, which is used to minimize an error function.
ML adjusts weights of hidden layers during training to minimize an error function [ 15].
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An optimal set of parameters for defining the functions is learned from examples by minimizing an error functional.
This optimization was based on minimizing an error function given by the difference between the experimental and analytical frequencies.
The procedure is based on a numerical experimental optimisation process which minimizes an error function defined by subtracting the experimental data from the outputs of the numerical analysis.
Model selection is based on minimizing an error measure which separates parametric uncertainty from structural errors to trade-off exploration with exploitation.
The proposed model can be trained by minimizing an error function and it allows an effective and intuitive initialization which avoids poor local minima.
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