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The split process tries to minimize the modeling error while the merge process decreases the model complexity, i.e., the number of line segments compared to the total number of points in the set.
In order to improve the model accuracy, neural network technique including back-propagation and gradient descent method were used to train the fuzzy parameters to minimize the modeling error function.
To minimize the modeling "noise" between OX receptors and to generate equal starting points for further study, we used the hOX1 model as the template to model hOX2.
Using the training set, each coefficient is adjusted to minimize the modeling error by the gradient descent method.
The proposed channel parameter estimator performs multipath mitigation in two ways: First, the maximum-likelihood estimator is able to take all relevant multipath components into account in order to minimize the modeling error.
We can minimize the modeling error with the accumulation of more biological knowledge.
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Our study aims to develop appropriate prior distributions and likelihood functions that minimize the model uncertainties and bias within a Bayesian ecohydrological modeling framework based on a traditional Pareto-based model calibration technique.
A modified genetic algorithm was also developed and used as an optimization tool to minimize the model error.
This tutorial describes how to select model parameters such as the number of topics by a (computationally intensive) tuning procedure, which searches for the parameters that minimize the model's perplexity on held-out data.
A Λ-optimal design was introduced to minimize the model parameter estimation error and to maximize the performance of the fermentation process.
In a sense, a model face is morphed into a target image so as to minimize the model fitting residual.
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