… example, a linear regression (eg, least squares regression) makes assumptions regarding
a linear … We focus on the SVR applications that use neuroimaging information, in particular …

… for many hydrological applications in providing information for optimal use of water … machine
(OP-ELM), least square support vector machine (LSSVM), multivariate adaptive regression …

… vector machine (LS-SVM) In this paper, the support vector machine (SVM) is used to predict
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… on least-square support vector machine and an autoregressive integrated moving average.
This hybrid approach’s forecast results are compared with multiple linear regression … uses a …

… In order to use radial basis function (RBF) kernel in the modeling, two tuning parameters of
… compared to support vector regression (SVR) is the use of the linear squares principle for the …

… , least squares support vector regression (LSSVR) [65], as a powerful nonlinear and
multivariate … Similar to support vector machine (SVM), LSSVR models are constructed via the …

… -rank-linear (MRL) filters and to fine tune these parameters. The least mean squares (LMS) …
The work carried out in [18] uses the least square SVM (LS-SVM) with an integration of …

… The objective of this study is to use Unmanned Aerial Vehicle (… /multiple linear regression,
Random Forests, and support … [24] (R 2 = 0.60) compared to partial least squares regression (…

… , more number of developers tend to apply Machine Learning (ML) and alternate methodologies
to … At the initial stage, the method undergoes training under the application of time series …

… Thus, the GA algorithm is used to optimize the parameters in support vector machine
regression model (SVR) in this paper, and the GA-SVR model is established through the coupling …