Kernel smoothing methods fall into the second category. Whereas the first choice is useful for globally characterizing complex functions, the second is very handy for temporal data and is able to include inner-state subject variations. Also, interactions among stimuli are considered. We compare state...
In this paper, we applied support vector regression to predict the number of COVID-19 cases for the 12 most-affected countries, testing for different structures of nonlinearity using Kernel functions and analyzing the sensitivity of the models’ predictive performance to different hyperparameters settin...
Considered herein is the well-posedness, asymptotic stability and blow-up of the initial-boundary value problem for nonlocal singular viscoelastic wave equation with logarithmic nonlinearityutt−1x(xux)x−1x(xuxt)x+∫0tm(t−λ)1x(xux(x,λ))xdλ=|u|r−2uln|u|subject to a no...
Pedotransfer function (PTF) approach is a convenient way for estimating difficult-to-measure soil properties from basic soil data. Typically, PTFs are developed using a large number of samples collected from small (regional) areas for training and testin
Nonlinear time series methods have improved in their usefulness in analyzing deterministic, nonlinear systems, and the feasibility of their application to fMRI data should be investigated. It is insufficient to state that their use in fMRI data analysis is justified by the fact that the brain is ...
Materials and methods In the present study, the experimental sample was virgin pumpkin seed oil, which is commonly used in the health food, food marketing, and cosmetic industries as an edible vegetable oil. This oil is rich in essential fatty acids, polyunsaturated fatty acids, natural ...
multiple kernel learningThis article proposes to develop a prediction model for traffic flow using kernel learning methods such as support vector machine (SVM) and multiple kernel learning (MKL). Traffic flow prediction is a dynamic problem owing to its complex nature of multicriteria and ...
Although derivation of the method involves gradient and kernel least squares techniques, the resulting algorithm possesses explicit form and does not utilize any time-consuming computational procedures. Theoretical aspects of the approach and comparison of the algorithm with other semi- and nonparametric ...
Henceforward [Math Processing Error]Bε(x) denotes the ball of [Math Processing Error]RN centered at [Math Processing Error]x∈RN and radius [Math Processing Error]ε>0. Throughout the paper, we always assume that the singular kernel [Math Processing...
whereΩ⊆R2is a bounded domain with a smooth boundary∂Ω. The vectorνis the unit outer normal to∂Ωand the constantkis a small positive real number. The functiongis the kernel and satisfies some conditions to be specified later. ...