Kernel‐based measures of association
Sign Up to like & getrecommendations! Published in 2018 at "Wiley Interdisciplinary Reviews: Computational Statistics"
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;
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
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
An Overview of Gradient-Enhanced Metamodels with Applications
Sign Up to like & getrecommendations! Published in 2019 at "Archives of Computational Methods in Engineering"
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
A modified bootstrap for kernel-based specification test with heavy-tailed data
Sign Up to like & getrecommendations! Published in 2020 at "Economics Letters"
DOI: 10.1016/j.econlet.2020.108986
Abstract: Abstract This paper provides a new resampling strategy to improve the finite sample performance of a nonparametric kernel-based specification test in the presence of heavy-tailed error terms. Based on the test statistic of Li and… read more here.
Keywords: kernel based; heavy tailed; test; specification test ... See more keywords
A kernel-based method to calculate local field potentials from networks of spiking neurons
Sign Up to like & getrecommendations! Published in 2020 at "Journal of Neuroscience Methods"
DOI: 10.1016/j.jneumeth.2020.108871
Abstract: BACKGROUND The local field potential (LFP) is usually calculated from current sources arising from transmembrane currents, in particular in asymmetric cellular morphologies such as pyramidal neurons. NEW METHOD Here, we adopt a different point of… read more here.
Keywords: networks spiking; kernel based; local field; based method ... See more keywords
A kernel-based weight decorrelation for regularizing CNNs
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DOI: 10.1016/j.neucom.2020.11.065
Abstract: Abstract Recent years has witnessed the success of convolutional neural networks (CNNs) in many machine learning and pattern recognition applications, especially in image recognition. However, due to the increasing model complexity, the parameter redundancy problem… read more here.
Keywords: cnns; kernel based; decorrelation regularizing; based weight ... See more keywords
Experimental kernel-based quantum machine learning in finite feature space
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DOI: 10.1038/s41598-020-68911-5
Abstract: We implement an all-optical setup demonstrating kernel-based quantum machine learning for two-dimensional classification problems. In this hybrid approach, kernel evaluations are outsourced to projective measurements on suitably designed quantum states encoding the training data, while… read more here.
Keywords: machine learning; quantum machine; space; kernel based ... See more keywords
Kernel-based estimation of P(X < Y) when X and Y are dependent random variables based on progressive type II censoring
Sign Up to like & getrecommendations! Published in 2020 at "Communications in Statistics - Theory and Methods"
DOI: 10.1080/03610926.2020.1774058
Abstract: The most widely used approach for reliability estimation is the well-known stress-strength model, θ = P(X read more here.
Keywords: estimation; random variables; kernel based; based estimation ... See more keywords