Results 51 to 60 of about 2,848,958 (338)
Anomaly Detection Using Local Kernel Density Estimation and Context-Based Regression
IEEE Transactions on Knowledge and Data Engineering, 2020 Current local density-based anomaly detection methods are limited in that the local density estimation and the neighborhood density estimation are not accurate enough for complex and large databases, and the detection performance depends on the size ...
Weiming Hu +5 more
semanticscholar +1 more source
Kernel density estimation based sampling for imbalanced class distribution [PDF]
Information Sciences, 2019 Imbalanced response variable distribution is a common occurrence in data science. In fields such as fraud detection, medical diagnostics, system intrusion detection and many others where abnormal behavior is rarely observed the data under study often ...
Firuz Kamalov
semanticscholar +1 more source
A new family of kernels from the beta polynomial kernels with applications in density estimation
IJAIN (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
A Kernel-Based Calculation of Information on a Metric Space
, 2013 Kernel density estimation is a technique for approximating probability distributions. Here, it is applied to the calculation of mutual information on a metric space.
Houghton, Conor J., Tobin, R. Joshua
core +2 more sources
The McCance Brain Care Score and Mortality: Evidence From a Large‐Scale Population‐Based Cohort
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objectives This study aimed to examine the relationship between the McCance Brain Care Score (BCS) and mortality in the general population. Methods We conducted a prospective, population‐based cohort study using data from the UK Biobank. Participants with complete data enabling calculation of BCS and full mortality information were included ...
Zhiqiang Xu, Xiaoxiao Wang, Nan Li
wiley +1 more source
Unsupervised Blur Kernel Estimation and Correction for Blind Super-Resolution
IEEE Access, 2022 Blind super-resolution (blind-SR) is an important task in the field of computer vision and has various applications in real-world. Blur kernel estimation is the main element of blind-SR along with the adaptive SR networks and a more accurately estimated ...
Youngsoo Kim +3 more
doaj +1 more source
Large-Margin Determinantal Point Processes [PDF]
, 2014 Determinantal point processes (DPPs) offer a powerful approach to modeling diversity in many applications where the goal is to select a diverse subset.
Chao, Wei-lun +3 more
core
Dynamic Kernel Distillation for Efficient Pose Estimation in Videos [PDF]
IEEE International Conference on Computer Vision, 2019 Existing video-based human pose estimation methods extensively apply large networks onto every frame in the video to localize body joints, which suffer high computational cost and hardly meet the low-latency requirement in realistic applications.
Xuecheng Nie +4 more
semanticscholar +1 more source
Glymphatic Dysfunction Reflects Post‐Concussion Symptoms: Changes Within 1 Month and After 3 Months
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Mild traumatic brain injury (mTBI) may alter glymphatic function; however, its progression and variability remain obscure. This study examined glymphatic function following mTBI within 1 month and after 3 months post‐injury to determine whether variations in glymphatic function are associated with post‐traumatic symptom severity ...
Eunkyung Kim +3 more
wiley +1 more source
IET Wireless Sensor SystemsThis paper introduces a kernel‐based approach to enhance the security of distributed estimation in the presence of adversary links. Adversary links often degrade distributed recovery algorithm performance in distributed estimation.
Zahra Khoshkalam +2 more
doaj +1 more source