Sentence examples for solved regression from inspiring English sources

Unlike the methods presented above, which require fitting a series of independently solved regression equations, simultaneous equation modelling theoretically represents a more elegant approach in which the distribution of each species accounts for the influence of all other species at the same time, and interactions between species need not be considered separately.

A similar idea also applies to solving regression problems.

Applying the SVM to solve regression problems is called the support vector regression (SVR) method (Basak et al. 2007).

The effectiveness of genetic programming (GP) for solving regression problems in hydrology has been recognized in recent studies.

Hybrid Bayesian networks are an appropriate approach to solve regression problems without such limitations, and they also provide additional advantages.

Recently, SVR has also been involved in radar areas to solve regression problems, such as prediction of the vehicle travel time [21] and parameter estimation in GPR [22 24].

Artificial neural networks (ANN) such as the support vector machine (SVM) and back propagation neural network (BPNN) has been widely employed to solve regression problems [18 22].

The experimental results demonstrate that proposed Boosting DAL-SVR method is effective to cover the shortage of deep learning in solving regression problems.

Least Square Support Vector Machine(LSSVM) has been widely used for solving regression and classification problems due to its simple solution.

This paper proposes a novel McSLM, called Recurrent Interval-Valued Metacognitive Scaffolding Fuzzy Neural Network (RIVMcSFNN) and used to solve regression and time-series modelling problems from data streams.

On this basis, we are able to examine in the study a new concept of semantics-based interpretability of FRBSs and develop a genetic method to design interpretable FRBSs to solve regression problems, for instance.