Results 61 to 70 of about 4,141 (227)
Combining kernelised autoencoding and centroid prediction for dynamic multi‐objective optimisation
CAAI Transactions on Intelligence Technology, EarlyView.Abstract Evolutionary algorithms face significant challenges when dealing with dynamic multi‐objective optimisation because Pareto optimal solutions and/or Pareto optimal fronts change. The authors propose a unified paradigm, which combines the kernelised autoncoding evolutionary search and the centroid‐based prediction (denoted by KAEP), for solving ...
Zhanglu Hou +4 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
, 2018 On the basis of a reproducing kernel Hilbert space, reproducing kernel functions for solving the coefficient inverse problem for the kinetic equation are given in this paper.
Esra Karatas Akgül
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The oscillatory response of the electroretinogram and neuronal adaptation
Acta Ophthalmologica, EarlyView.Abstract After more than 50 years, there still remains a challenge and an interest to know more as well as extend and deepen our understanding of the small rapid wavelets, the oscillatory potentials (OPs), of the electroretinogram (ERG) and the neuronal adaptation of the retina.
Lillemor Wachtmeister, Anders Eklund
wiley +1 more source
Density Problem and Approximation Error in Learning Theory
Abstract and Applied Analysis, 2013 We study the density problem and approximation error of reproducing kernel Hilbert spaces for the purpose of learning theory. For a Mercer kernel on a compact metric space (, ), a characterization for the generated reproducing kernel Hilbert space (RKHS)
Ding-Xuan Zhou
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A new application of reproducing kernel Hilbert space method
, 2018 We demonstrate a new application of the reproducing kernel Hilbert space method in this paper.
A. Akgül +3 more
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Journal of Time Series Analysis, EarlyView.ABSTRACT Expectile is a coherent and elicitable law‐invariant risk measure widely applied in risk management. Existing methods based on iteratively reweighted least squares (IWLS) are not computationally efficient for large‐scale sample sizes. To overcome the issue, we develop a direct nonparametric conditional expectile function estimator by inverting
Feipeng Zhang, Ping‐Shou Zhong
wiley +1 more source
Ain Shams Engineering Journal, 2018 In this article, we introduce a novel numerical scheme, the iterative reproducing kernel method (IRKM), for providing numerical approximate solutions of a certain class of time-fractional boundary value problem within favorable aspects of the reproducing
Mohammed Al-Smadi
doaj +1 more source
The $L^\infty$ Learnability of Reproducing Kernel Hilbert Spaces
CoRR, 2023 In this work, we analyze the learnability of reproducing kernel Hilbert spaces (RKHS) under the $L^\infty$ norm, which is critical for understanding the performance of kernel methods and random feature models in safety- and security-critical applications.
Hongrui Chen, Jihao Long, Lei Wu
openaire +2 more sources
Solutions of fractional differential equations with reproducing kernel hilbert space method
, 2022 Bu tez yedi bölümden oluşmaktadır. Birinci bölümde, kesir mertebeli türev ve doğuran çekirdekli Hilbert uzayı metodu hakkında tarihsel gelişim ve literatür taraması verilmiştir. İkinci bölümde, tez çalışmasında kullanılacak olan temel tanım, teorem ve ön
Şenol, Mehmet, Ata, Ayşe
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