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In SVR, the structural risk minimization is implemented for minimizing the upper bound of generalization error, while ANN takes the advantage of empirical risk minimization to minimize the prediction error (Asoodeh and Bagheripour 2013).
Although it is preferable to minimize the prediction error instead of predicting the error, the problem of minimization of the prediction error is as difficult as numerical weather prediction.
However, the algorithm may not necessarily minimize the prediction error of the test dataset itself; thus, some degree of mismatch between the predicted and actual fibrosis scores is expected.
The problem is resolved by using what is called a smearing factor to minimize the prediction error.
The algorithm behind EBK generates several semivariogram models to minimize the prediction error generated from the uncertainty of model parameters.
In the study, the model parameters are calibrated in order to minimize the prediction errors in a certain building.
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In this case, the optimal smoothing parameter λ of SEMD is selected as λ=0.0523by minimizing the prediction error of cross-validation.
We use a forward linear predictor, which as usual is obtained by minimizing the prediction error in the least squares sense, cf. [23] for details.
Finding the best value/type for each network parameter involves finding a value/type for that parameter which minimizes the prediction error.
The procedure is essentially based on imposing additional constraints on the allocation of eigenvalues of the filter's transition matrix and on minimizing the prediction error.
To validate the PLS model, the number of extracted factors was chosen through 10-fold cross validation, i.e. fitting the model to part of the data and minimizing the prediction error for the unfitted part [17].
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