Results 51 to 60 of about 2,581 (224)
Advanced Intelligent Discovery, EarlyView.The authors develop a deep learning model for real‐time tracking of wound progression. The deep learning framework maps the nonlinear evolution of a time series of images to a latent space, where they learn a linear representation of the dynamics. The linear model is interpretable and suitable for applications in feedback control.
Fan Lu +11 more
wiley +1 more source
On the stability test for reproducing kernel Hilbert spaces
CoRR, 2023 Reproducing kernel Hilbert spaces (RKHSs) are special Hilbert spaces where all the evaluation functionals are linear and bounded. They are in one-to-one correspondence with positive definite maps called kernels. Stable RKHSs enjoy the additional property of containing only functions and absolutely integrable.
Mauro Bisiacco, Gianluigi Pillonetto
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The Convergence Rate for a K-Functional in Learning Theory
Journal of Inequalities and Applications, 2010 It is known that in the field of learning theory based on reproducing kernel Hilbert spaces the upper bounds estimate for a K-functional is needed.
Bao-Huai Sheng, Dao-Hong Xiang
doaj +1 more source
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
Advances in causal discovery methods for ecological time series
Biological Reviews, EarlyView.ABSTRACT Recent advances in data collection technologies (e.g. automated sensor networks, satellite remote sensing, and high‐throughput sequencing) have greatly expanded the availability of ecological time series, enabling new opportunities for causal analyses in dynamic ecosystems.
Kenta Suzuki +6 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
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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
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n-Best kernel approximation in reproducing kernel Hilbert spaces
Applied and Computational Harmonic Analysis, 2022 By making a seminal use of the maximum modulus principle of holomorphic functions we prove existence of $n$-best kernel approximation for a wide class of reproducing kernel Hilbert spaces of holomorphic functions in the unit disc, and for the corresponding class of Bochner type spaces of stochastic processes.
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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