Search alternatives:
processing algorithm » modeling algorithm (Expand Search), routing algorithm (Expand Search), tracking algorithm (Expand Search)
method algorithms » two algorithms (Expand Search), both algorithms (Expand Search), other algorithms (Expand Search)
data processing » image processing (Expand Search)
data algorithm » data algorithms (Expand Search), update algorithm (Expand Search), atlas algorithm (Expand Search)
recent data » relevant data (Expand Search)
element » elements (Expand Search)
processing algorithm » modeling algorithm (Expand Search), routing algorithm (Expand Search), tracking algorithm (Expand Search)
method algorithms » two algorithms (Expand Search), both algorithms (Expand Search), other algorithms (Expand Search)
data processing » image processing (Expand Search)
data algorithm » data algorithms (Expand Search), update algorithm (Expand Search), atlas algorithm (Expand Search)
recent data » relevant data (Expand Search)
element » elements (Expand Search)
-
1
-
2
The run time for each algorithm in seconds.
Published 2025“…<div><p>In this paper, we study a class of non-parametric regression models for predicting graph signals as a function of explanatory variables . Recently, Kernel Graph Regression (KGR) and Gaussian Processes over Graph (GPoG) have emerged as promising techniques for this task. …”
-
3
Pareto optimal front result of MOCOA.
Published 2025“…<div><p>Recent research for arrhythmia classification is increasingly based on AI-driven approaches, which are primarily grounded in ECG data, but often neglect the mathematical foundations of cardiac electrophysiology. …”
-
4
Confusion matrix.
Published 2025“…<div><p>Recent research for arrhythmia classification is increasingly based on AI-driven approaches, which are primarily grounded in ECG data, but often neglect the mathematical foundations of cardiac electrophysiology. …”
-
5
Action potential of sample points in model 1.
Published 2025“…<div><p>Recent research for arrhythmia classification is increasingly based on AI-driven approaches, which are primarily grounded in ECG data, but often neglect the mathematical foundations of cardiac electrophysiology. …”
-
6
Performance validation on the MIT-BIH database.
Published 2025“…<div><p>Recent research for arrhythmia classification is increasingly based on AI-driven approaches, which are primarily grounded in ECG data, but often neglect the mathematical foundations of cardiac electrophysiology. …”
-
7
Exponentially attenuated sinusoidal function.
Published 2025“…<div><p>Recent research for arrhythmia classification is increasingly based on AI-driven approaches, which are primarily grounded in ECG data, but often neglect the mathematical foundations of cardiac electrophysiology. …”
-
8
Performance comparison with other papers.
Published 2025“…<div><p>Recent research for arrhythmia classification is increasingly based on AI-driven approaches, which are primarily grounded in ECG data, but often neglect the mathematical foundations of cardiac electrophysiology. …”
-
9
Action potential of sample points in model 2.
Published 2025“…<div><p>Recent research for arrhythmia classification is increasingly based on AI-driven approaches, which are primarily grounded in ECG data, but often neglect the mathematical foundations of cardiac electrophysiology. …”
-
10
Action potential of sample points in model 0.
Published 2025“…<div><p>Recent research for arrhythmia classification is increasingly based on AI-driven approaches, which are primarily grounded in ECG data, but often neglect the mathematical foundations of cardiac electrophysiology. …”
-
11
Mean squared Error on all unseen data.
Published 2025“…<div><p>In this paper, we study a class of non-parametric regression models for predicting graph signals as a function of explanatory variables . Recently, Kernel Graph Regression (KGR) and Gaussian Processes over Graph (GPoG) have emerged as promising techniques for this task. …”
-
12
Possible graph filter functions.
Published 2025“…<div><p>In this paper, we study a class of non-parametric regression models for predicting graph signals as a function of explanatory variables . Recently, Kernel Graph Regression (KGR) and Gaussian Processes over Graph (GPoG) have emerged as promising techniques for this task. …”
-
13
The notational conventions used in this paper.
Published 2025“…<div><p>In this paper, we study a class of non-parametric regression models for predicting graph signals as a function of explanatory variables . Recently, Kernel Graph Regression (KGR) and Gaussian Processes over Graph (GPoG) have emerged as promising techniques for this task. …”
-
14
Practical implementation of an End-to-end methodology for SPC of 3-D part geometry: A case study
Published 2025“…<p>Del Castillo and Zhao have recently proposed a new methodology for the Statistical Process Control (SPC) of discrete parts whose 3-dimensional (3D) geometrical data are acquired with non-contact sensors. …”
-
15
Data Sheet 1_Image-assisted textural analysis of plagioclase crystals in volcanic rocks: an application to lavas erupted on 2021 at Pacaya volcano, Guatemala.docx
Published 2025“…<p>The adoption of semi-automated image processing methods to investigate geo-petrological processes has grown quickly in recent years. …”
-
16
Table 1_Image-assisted textural analysis of plagioclase crystals in volcanic rocks: an application to lavas erupted on 2021 at Pacaya volcano, Guatemala.xlsx
Published 2025“…<p>The adoption of semi-automated image processing methods to investigate geo-petrological processes has grown quickly in recent years. …”