Sentence examples for model regression problem from inspiring English sources

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

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.

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).

We formulate the background and foreground model into a contiguously weighted linear regression problem.

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.

Multiple linear regression models are further adopted to solve the regression problem in the low-dimensional manifold space.

A solution of this regression problem is to learn a regression model from examples that compute Y given X.

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.

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