Results 11 to 20 of about 1,617,710 (291)

Connectome spatial smoothing (CSS): Concepts, methods, and evaluation

open access: yesNeuroImage, 2022
Structural connectomes are increasingly mapped at high spatial resolutions comprising many hundreds—if not thousands—of network nodes. However, high-resolution connectomes are particularly susceptible to image registration misalignment, tractography ...
Sina Mansour L   +3 more
doaj   +1 more source

Smoothing parameter selection in Nadaraya-Watson kernel nonparametric regression using nature-inspired algorithm optimization [PDF]

open access: yesالمجلة العراقية للعلوم الاحصائية, 2020
In the context of Nadaraya-Watson kernel nonparametric regression, the curve estimation is fully depending on the smoothing parameter. At this point, the nature-inspired algorithms can be used as an alternative tool to find the optimal selection. In this
Zinah Basheer, Zakariya Algamal
doaj   +1 more source

Distributed Smoothed Tree Kernel [PDF]

open access: yesItalian Journal of Computational Linguistics, 2014
In this paper we explore the possibility to merge the world of Compositional Distributional Semantic Models (CDSM) with Tree Kernels (TK). In particular, we will introduce a specific tree kernel (smoothed tree kernel, or STK) and then show that is possibile to approximate such kernel with the dot product of two vectors obtained compositionally from the
Ferrone, L, ZANZOTTO, FABIO MASSIMO
openaire   +5 more sources

Bayesian Spatial Kernel Smoothing for Scalable Dense Semantic Mapping [PDF]

open access: yesIEEE Robotics and Automation Letters, 2019
This article develops a Bayesian continuous 3D semantic occupancy map from noisy point clouds by generalizing the Bayesian kernel inference model for building occupancy maps, a binary problem, to semantic maps, a multi-class problem.
Lu Gan   +4 more
semanticscholar   +1 more source

Choice of Smoothing Parameter for Kernel Type Ridge Estimators in Semiparametric Regression Models

open access: yesRevstat Statistical Journal, 2021
This paper concerns kernel-type ridge estimators of parameters in a semiparametric model. These estimators are a generalization of the well-known Speckman’s approach based on kernel smoothing method. The most important factor in achieving this smoothing
Ersin Yilmaz   +2 more
doaj   +1 more source

Equivalent Kernels for Smoothing Splines [PDF]

open access: yesJournal of Integral Equations and Applications, 2006
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Eggermont, P.P.B., LaRiccia, V.N.
openaire   +2 more sources

Remaining useful life prediction of aircraft lithium-ion batteries based on F-distribution particle filter and kernel smoothing algorithm

open access: yesChinese Journal of Aeronautics, 2020
As an emergency and auxiliary power source for aircraft, lithium (Li)-ion batteries are important components of aerospace power systems. The Remaining Useful Life (RUL) prediction of Li-ion batteries is a key technology to ensure the reliable operation ...
Kai Zhang   +4 more
semanticscholar   +1 more source

A Fast Two-Stage Bilateral Filter Using Constant Time O(1) Histogram Generation

open access: yesSensors, 2022
Bilateral Filtering (BF) is an effective edge-preserving smoothing technique in image processing. However, an inherent problem of BF for image denoising is that it is challenging to differentiate image noise and details with the range kernel, thus often ...
Sheng-Wei Cheng   +2 more
doaj   +1 more source

A new family of kernels from the beta polynomial kernels with applications in density estimation

open access: yesIJAIN (International Journal of Advances in Intelligent Informatics), 2020
One of the fundamental data analytics tools in statistical estimation is the non-parametric kernel method that involves probability estimates production.
Israel Uzuazor Siloko   +2 more
doaj   +1 more source

Spatial Smoothing Effect on Group-Level Functional Connectivity during Resting and Task-Based fMRI

open access: yesSensors, 2023
Spatial smoothing is a preprocessing step applied to neuroimaging data to enhance data quality by reducing noise and artifacts. However, selecting an appropriate smoothing kernel size can be challenging as it can lead to undesired alterations in final ...
Cemre Candemir
doaj   +1 more source

Home - About - Disclaimer - Privacy