Histogram binwidth and kernel bandwidth selection for the spike-rate estimation [PDF]
BMC Neuroscience, 2009 Shinomoto Shigeru, Shimazaki Hideaki
doaj +2 more sources
Journal of nonparametric statistics (Print), 2022 In this paper, we derive asymptotic expressions for the mean integrated squared error of a class of delta sequence density estimators for circular data.
Carlos Tenreiro
semanticscholar +1 more source
Joint Client Selection and Bandwidth Allocation Algorithm for Federated Learning
IEEE Transactions on Mobile Computing, 2023 In federated learning (FL), if the participating mobile devices have low computing power and poor wireless channel conditions and/or they do not have sufficient data for various classes, a long convergence time is required to achieve the desired model ...
Haneul Ko +4 more
semanticscholar +1 more source
A new bandwidth selection criterion for using SVDD to analyze hyperspectral data [PDF]
Defense + Security, 2018 This paper presents a method for hyperspectral image classification that uses support vector data description (SVDD) with the Gaussian kernel function. SVDD has been a popular machine learning technique for single-class classification, but selecting the ...
Deovrat Kakde +5 more
openalex +3 more sources
Edge-Preserving Denoising of Image Sequences
Entropy, 2021 To monitor the Earth’s surface, the satellite of the NASA Landsat program provides us image sequences of any region on the Earth constantly over time.
Fan Yi, Peihua Qiu
doaj +1 more source
New Gradient Methods for Bandwidth Selection in Bivariate Kernel Density Estimation [PDF]
, 2018 The bivariate kernel density estimator is fundamental in data smoothing methods especially for data exploration and visualization purposes due to its ease of graphical interpretation of results.
Israel Uzuazor Siloko +2 more
openalex +2 more sources
Data-Driven Participant Selection and Bandwidth Allocation for Heterogeneous Federated Edge Learning
IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2023 Federated edge learning (FEEL) is a rapidly growing distributed learning technique for next-generation wireless edge systems. Smart systems across various application domains face challenges, such as data heterogeneity, limited wireless resources, and ...
Abdullatif Albaseer +3 more
semanticscholar +1 more source
lpdensity: Local Polynomial Density Estimation and Inference
Journal of Statistical Software, 2022 Density estimation and inference methods are widely used in empirical work. When the underlying distribution has compact support, conventional kernel-based density estimators are no longer consistent near or at the boundary because of their well-known ...
Matias D. Cattaneo +2 more
doaj +1 more source
Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: A Long-Term Perspective [PDF]
IEEE Transactions on Wireless Communications, 2020 This paper studies federated learning (FL) in a classic wireless network, where learning clients share a common wireless link to a coordinating server to perform federated model training using their local data.
Jie Xu, Heqiang Wang
semanticscholar +1 more source
Simultaneous Selection of Optimal Bandwidths for the Sharp Regression Discontinuity Estimator [PDF]
, 2015 A new bandwidth selection rule that uses different bandwidths for the local linear regression estimators on the left and the right of the cut-off point is proposed for the sharp regression discontinuity estimator of the mean program impact at the cut-off
Arai, Yoichi, Ichimura, Hidehiko
core +2 more sources