An effective implementation for kernel‐based positive system identification using Gibbs sampling
Sign Up to like & getrecommendations! Published in 2025 at "Asian Journal of Control"
DOI: 10.1002/asjc.3682
Abstract: Recently, the kernel‐based method has been applied for the positive system identification where the hyperparameter estimation is a crucial and critical part. The regularized identification problem for the positive system is first formulated. Due to… read more here.
Keywords: system; positive system; gibbs sampling; kernel based ... See more keywords
Kernel‐Based Bootstrap Synthetic Data to Estimate Measurement Uncertainty in Analytical Sciences
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DOI: 10.1002/cem.3628
Abstract: Measurement uncertainty (MU) is becoming a key figure of merit for analytical methods, and estimating MU from method validation data is cost‐effective and practical. Since MU can be defined as a coverage interval of a… read more here.
Keywords: kernel based; synthetic data; uncertainty; validation data ... See more keywords
Kernel‐based measures of association
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DOI: 10.1002/wics.1422
Abstract: A general framework for association measures that unifies existing methods and guides derivation of novel measures for complex data types. read more here.
Keywords: based measures; measures association; kernel based;
On the convergence of generalized kernel-based interpolation by greedy data selection algorithms
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DOI: 10.1007/s10543-024-01048-3
Abstract: We analyze the convergence of generalized kernel-based interpolation methods. This is done under minimalistic assumptions on both the kernel and the target function. On these grounds, we further prove convergence of popular greedy data selection… read more here.
Keywords: generalized kernel; convergence generalized; kernel based; greedy data ... See more keywords
Fuzzy clustering using multiple Gaussian kernels with optimized-parameters
Sign Up to like & getrecommendations! Published in 2018 at "Fuzzy Optimization and Decision Making"
DOI: 10.1007/s10700-017-9268-x
Abstract: In this paper, we propose a new kernel-based fuzzy clustering algorithm which tries to find the best clustering results using optimal parameters of each kernel in each cluster. It is known that data with nonlinear… read more here.
Keywords: fuzzy clustering; kernel based; based fuzzy; single kernel ... See more keywords
Numerical Aspects of the Tensor Product Multilevel Method for High-Dimensional, Kernel-Based Reconstruction on Sparse Grids
Sign Up to like & getrecommendations! Published in 2025 at "Journal of Scientific Computing"
DOI: 10.1007/s10915-025-03144-0
Abstract: This paper investigates the approximation of functions with finite smoothness defined on domains with a Cartesian product structure. The recently proposed tensor product multilevel method (TPML) combines Smolyak’s sparse grid method with a kernel-based residual… read more here.
Keywords: product; product multilevel; tensor product; kernel based ... See more keywords
Kernel-Based Subspace Learning on Riemannian Manifolds for Visual Recognition
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DOI: 10.1007/s11063-019-10083-z
Abstract: Covariance matrices have attracted increasing attention for data representation in many computer vision tasks. The nonsingular covariance matrices are regarded as points on Riemannian manifolds rather than Euclidean space. A common technique for classification on… read more here.
Keywords: rkhs; space; kernel based; euclidean space ... See more keywords
Hebbian Learning with Kernel-Based Embedding of Input Data
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DOI: 10.1007/s11063-024-11707-9
Abstract: Although it requires simple computations, provides good performance on linear classification tasks and offers a suitable environment for active learning strategies, the Hebbian learning rule is very sensitive to how the training data relate to… read more here.
Keywords: input data; input; hebbian learning; based embedding ... See more keywords
An Overview of Gradient-Enhanced Metamodels with Applications
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DOI: 10.1007/s11831-017-9226-3
Abstract: Metamodeling, the science of modeling functions observed at a finite number of points, benefits from all auxiliary information it can account for. Function gradients are a common auxiliary information and are useful for predicting functions… read more here.
Keywords: kernel based; overview gradient; enhanced metamodels; gradient enhanced ... See more keywords
A Kernel-Based Extreme Learning Machine Framework for Classification of Hyperspectral Images Using Active Learning
Sign Up to like & getrecommendations! Published in 2019 at "Journal of the Indian Society of Remote Sensing"
DOI: 10.1007/s12524-019-01021-6
Abstract: The rapid development of advanced remote sensing technology with multichannel imaging sensors has increased its potential opportunity in the utilization of hyperspectral data for various applications. For supervised classification of hyperspectral data, obtaining suitable training… read more here.
Keywords: machine; classification; kernel based; classification hyperspectral ... See more keywords
Kernel-based testing with skewed and heavy-tailed data: Evidence from a nonparametric test for heteroskedasticity
Sign Up to like & getrecommendations! Published in 2018 at "Economics Letters"
DOI: 10.1016/j.econlet.2018.08.007
Abstract: We examine the performance of a nonparametric kernel-based specification test in the presence of skewed and heavy-tailed regressors. We start by modifying the Zheng (2009) test for heteroskedasticity by removing the random denominator in the… read more here.
Keywords: kernel based; test heteroskedasticity; heavy tailed; test ... See more keywords