Sentence examples for model regression problems from inspiring English sources

Thus, this work presents a new radial-basis network (RBN) design that overcomes the limitations of using ANNs to accurately model regression problems with minimal training data.

To perform multiclasses classification, SLFNs generally utilize the One-Against-All (OAA) method to transform the classification application to a multioutput model regression problem.

This paper presents a probabilistic-entropy-based neural network (PENN) model for tackling online data regression problems.

In the nonlinear case, this destroys the linear regression structure of the problem even if the control filter is linear-in-the-parameters, thereby making it impossible to apply the many existing model selection methods for linear regression problems.

For the purpose of solving endogeneity problems of general model regression, the first-order difference method is adopted based on the fixed effect hypothesis to eliminate individual heterogeneity, which fails to vary from the model with time, so that the real income effect of occupational interaction can be acquired.

Penalized likelihood methods can be applied to these high dimensional regression problems to perform model selection.

Bertsimas and Shioda (2007) presented mixed integer programming or MIP models for the classification and robust regression problems.

Bayesian model class selection is particularly useful for regression problems since the regression formula order is difficult to be determined solely by physics due to its empirical nature.

For most regression problems, the optimal regression model can be obtained by minimizing a loss function, and the selection of loss functions has great effect on the performance of the derived regression model.

Hence, bagging involves training different models with different samples and usually predictions are obtained by averaging the results of the different base models for a regression problem.

Finding this function is modeled as a regression problem and can be solved by Support Vector regression (SVR).