Results 41 to 50 of about 16,578 (196)
Advanced Intelligent Systems, EarlyView.Four decades of retinal vessel segmentation research (1982–2025) are synthesized, spanning classical image processing, machine learning, and deep learning paradigms. A meta‐analysis of 428 studies establishes a unified taxonomy and highlights performance trends, generalization capabilities, and clinical relevance.
Avinash Bansal +6 more
wiley +1 more source
Some Notes on Error Analysis for Kernel Based Regularized Interpolation
International Journal of Analysis and Applications, 2020 Kernel based regularized interpolation is one of the most important methods for approximating functions. The theory behind the kernel based regularized interpolation is the well-known Representer Theorem, which shows the form of approximation function in
Qing Zou
doaj
Error Bound of Mode-Based Additive Models
Entropy, 2021 Due to their flexibility and interpretability, additive models are powerful tools for high-dimensional mean regression and variable selection. However, the least-squares loss-based mean regression models suffer from sensitivity to non-Gaussian noises ...
Hao Deng +3 more
doaj +1 more source
International Journal of Eating Disorders, EarlyView.ABSTRACT Objective The efficacy of psychological therapies for adolescents and adults with avoidant/restrictive food intake disorder (ARFID) has yet to be rigorously analyzed through systematic review or meta‐analysis. Method We identified articles from seven databases that presented psychological therapies for adolescents and adults with ARFID. First,
Copeland G. Winten +4 more
wiley +1 more source
Reproducing Kernel Hilbert Space and Coalescence Hidden-variable Fractal Interpolation Functions
Demonstratio Mathematica, 2019 Reproducing Kernel Hilbert Spaces (RKHS) and their kernel are important tools which have been found to be incredibly useful in many areas like machine learning, complex analysis, probability theory, group representation theory and the theory of integral ...
Prasad Srijanani Anurag
doaj +1 more source
Analysis of unbounded operators and random motion
, 2009 We study infinite weighted graphs with view to \textquotedblleft limits at infinity,\textquotedblright or boundaries at infinity. Examples of such weighted graphs arise in infinite (in practice, that means \textquotedblleft very\textquotedblright large ...
Dunford N. +6 more
core +1 more source
Operator inequalities in reproducing kernel Hilbert spaces
Communications Faculty Of Science University of Ankara Series A1Mathematics and Statistics, 2022 Summary: In this paper, by using some classical Mulholland type inequality, Berezin symbols and reproducing kernel technique, we prove the power inequalities for the Berezin number \(\operatorname{ber}(A)\) for some self-adjoint operators \(A\) on \({H}(\Omega)\).
openaire +5 more sources
Mathematical Methods in the Applied Sciences, EarlyView.A highly accurate numerical method is given for the solution of boundary value problem of generalized Bagley‐Torvik (BgT) equation with Caputo derivative of order 0<β<2$$ 0<\beta <2 $$ by using the collocation‐shooting method (C‐SM). The collocation solution is constructed in the space Sm+1(1)$$ {S}_{m+1}^{(1)} $$ as piecewise polynomials of degree at ...
Suzan Cival Buranay +2 more
wiley +1 more source
Alexandria Engineering Journal, 2021 In this paper, a coupled system of fractional differential equations along with integral boundary conditions is discussed by means of the iterative reproducing kernel algorithm.
Rania Saadeh
doaj +1 more source
Inference in Nonlinear Differential Equations [PDF]
, 2015 Parameter inference in mechanistic models of coupled differential equations is a challenging problem. We propose a new method using kernel ridge regression in Reproducing Kernel Hilbert Spaces (RKHS). A three-step gradient matching algorithm is developed
Filippone, Maurizio +3 more
core