Results 21 to 30 of about 45,420 (307)
CoRR, 2020 Image acquisition and segmentation are likely to introduce noise. Further image processing such as image registration and parameterization can introduce additional noise. It is thus imperative to reduce noise measurements and boost signal. In order to increase the signal-to-noise ratio (SNR) and smoothness of data required for the subsequent random ...
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Kernel Density Estimators for Gaussian Mixture Models
Lithuanian Journal of Statistics, 2013 The problem of nonparametric estimation of probability density function is considered. The performance of kernel estimators based on various common kernels and a new kernel K (see (14)) with both fixed and adaptive smoothing bandwidth is compared in ...
Tomas Ruzgas, Indrė Drulytė
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Effects of spatial smoothing on group-level differences in functional brain networks
Network Neuroscience, 2020 Brain connectivity with functional magnetic resonance imaging (fMRI) is a popular approach for detecting differences between healthy and clinical populations.
Ana María Triana +3 more
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Nonparametric estimate remarks
Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 2006 Kernel smoothers belong to the most popular nonparametric functional estimates. They provide a simple way of finding structure in data. The idea of the kernel smoothing can be applied to a simple fixed design regression model.
Jitka Poměnková
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Peramalan Permintaan Inti Sawit (Kernel) di PT. Perkebunan Nusantara V Sei Pagar
Jurnal Teknik Industri: Jurnal Hasil Penelitian dan Karya Ilmiah dalam Bidang Teknik Industri, 2018 Produksi inti sawit (kernel) yang berlebih di PT. Perkebunan Nusantara V Sei Pagar mengindikasikan produksi yang tidak direncanakan dengan baik. Akibatnya terdapat penumpukan sisa penjualan selama tahun 2016 yang jumlahnya mengalami kenaikan 2 kali lipat
Nofirza Nofirza
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Regression with Ordered Predictors via Ordinal Smoothing Splines
Frontiers in Applied Mathematics and Statistics, 2017 Many applied studies collect one or more ordered categorical predictors, which do not fit neatly within classic regression frameworks. In most cases, ordinal predictors are treated as either nominal (unordered) variables or metric (continuous) variables ...
Nathaniel E. Helwig, Nathaniel E. Helwig
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The Structurally Smoothed Graphlet Kernel
CoRR, 2014 A commonly used paradigm for representing graphs is to use a vector that contains normalized frequencies of occurrence of certain motifs or sub-graphs. This vector representation can be used in a variety of applications, such as, for computing similarity between graphs. The graphlet kernel of Shervashidze et al. [32] uses induced sub-graphs of k nodes (
Pinar Yanardag, S. V. N. Vishwanathan
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Smooth ECE: Principled Reliability Diagrams via Kernel Smoothing
CoRR, 2023 Calibration measures and reliability diagrams are two fundamental tools for measuring and interpreting the calibration of probabilistic predictors. Calibration measures quantify the degree of miscalibration, and reliability diagrams visualize the structure of this miscalibration.
Jaroslaw Blasiok, Preetum Nakkiran
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Computation, 2021 This article deals with the problem of designing regression models for evaluating the parameters of the operation of complex technological equipment—hydroturbine units.
Vladimir Viktorovich Bukhtoyarov +1 more
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Solving 2D Poisson-type equations using meshless SPH method
Results in Physics, 2019 In the present study, 2D Poisson-type equation is solved by a meshless Symmetric Smoothed Particle Hydrodynamics (SSPH) method. The influence of the kernel function, smoothing length and particle discretizations of problem domain on the solutions of ...
Shuai Liu +6 more
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