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The Megopolis resampler: Memory coalesced resampling on GPUs [PDF]
Digital Signal Processing, 2022 The resampling process employed in widely used methods such as Importance Sampling (IS), with its adaptive extension (AIS), are used to solve challenging problems requiring approximate inference; for example, non-linear, non-Gaussian state estimation problems.
Joshua A. Chesser +2 more
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Computers, 2023 Machine Learning is widely used in cybersecurity for detecting network intrusions. Though network attacks are increasing steadily, the percentage of such attacks to actual network traffic is significantly less.
Sikha S. Bagui +3 more
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Detection of Credit Card Fraud with Machine Learning Methods and Resampling Techniques
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 2022 Financial institutions in the form of banks provide facilities in the form of credit cards, but with the development of technology, fraud on credit card transactions is still common, so a system is needed that can detect fraud transactions quickly and ...
Moh. Badris Sholeh Rahmatullah +4 more
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Applied Sciences, 2022 Land use/land cover (LULC) has an important impact on the ecological environment and is crucial for calculating ecosystem service values (ESVs). However, whether and to what extent the ESVs vary when calculated by LULC product data at different spatial ...
Ziwen Huo +3 more
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IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 2023 Cardiovascular disease (CVD) is the leading cause of death worldwide. Primary prevention is by early prediction of the disease onset. Using laboratory data from the National Health and Nutrition Examination Survey (NHANES) in 2017-2020 timeframe (N= 7 ...
Fadlan Hamid Alfebi, Mila Desi Anasanti
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GEOSTATISTICAL MODELING OF SOYBEAN YIELD AND SOIL CHEMICAL ATTRIBUTES USING SPATIAL BOOTSTRAP [PDF]
Engenharia Agrícola, 2019 The goal of this study was to use the spatial bootstrap method to model the spatial dependence structure of soybean yield and soil chemical attributes in an agricultural area.
Gustavo H. Dalposso +4 more
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Adaptive memory-based single distribution resampling for particle filter
Journal of Big Data, 2017 The restrictions that are related to using single distribution resampling for some specific computing devices’ memory gives developers several difficulties as a result of the increased effort and time needed for the development of a particle filter. Thus,
Wan Mohd Yaakob Wan Bejuri +4 more
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Remote Sensing, 2023 Geostationary satellite-based remote sensing is a powerful tool to observe and understand the spatiotemporal variation of cloud optical-microphysical properties and their climatologies.
Dongchen Li, Masanori Saito, Ping Yang
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Fingerprint resampling: A generic method for efficient resampling [PDF]
Scientific Reports, 2015 AbstractIn resampling methods, such as bootstrapping or cross validation, a very similar computational problem (usually an optimization procedure) is solved over and over again for a set of very similar data sets. If it is computationally burdensome to solve this computational problem once, the whole resampling method can become unfeasible.
Mestdagh, Merijn +3 more
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Negative association, ordering and convergence of resampling methods [PDF]
, 2019 We study convergence and convergence rates for resampling schemes. Our first main result is a general consistency theorem based on the notion of negative association, which is applied to establish the almost-sure weak convergence of measures output from ...
Chopin, Nicolas +2 more
core +4 more sources