Zobrazeno 1 - 10
of 724
pro vyhledávání: '"Tsutsumida A"'
Autor:
Chenan, Huang, Tsutsumida, Narumasa
Unsupervised clustering has emerged as a critical tool for uncovering hidden patterns and insights from vast, unlabeled datasets. However, traditional methods like Partitioning Around Medoids (PAM) struggle with scalability due to their quadratic com
Externí odkaz:
http://arxiv.org/abs/2408.16993
Autor:
Tsutsumida, Narumasa, Kato, Akira
Land cover classification plays a critical role in describing Earth's surface characteristics. However, these classifications can be affected by uncertainties introduced by variability in investigator interpretations. While land cover classification
Externí odkaz:
http://arxiv.org/abs/2403.15720
Autor:
Murakami, Daisuke, Tsutsumida, Narumasa, Yoshida, Takahiro, Nakaya, Tomoki, Lu, Binbin, Harris, Paul
Although geographically weighted Poisson regression (GWPR) is a popular regression for spatially indexed count data, its development is relatively limited compared to that found for linear geographically weighted regression (GWR), where many extensio
Externí odkaz:
http://arxiv.org/abs/2305.08443
Autor:
Percival, Joseph Emile Honour, Tsutsumida, Narumasa, Murakami, Daisuke, Yoshida, Takahiro, Nakaya, Tomoki
Exploratory spatial data analysis (ESDA) plays a key role in research that includes geographic data. In ESDA, analysts often want to be able to visualize observations and local relationships on a map. However, software dedicated to visualizing local
Externí odkaz:
http://arxiv.org/abs/2101.03491
Publikováno v:
Frontiers in Sustainable Tourism, Vol 3 (2024)
To further develop the accuracy of monitoring cherry flowering dates, we require phenological records from multiple points in multiple years at the catchment scale, as well as conventional in situ phenological observations, phenological data publishe
Externí odkaz:
https://doaj.org/article/48a9d21c01ce4120acbb9669e8560c22
Autor:
Tsutsumida, Narumasa, Funada, Shuya
Publikováno v:
In Ecological Informatics December 2023 78
Although a number of studies have developed fast geographically weighted regression (GWR) algorithms for large samples, none of them has achieved linear-time estimation, which is considered a requisite for big data analysis in machine learning, geost
Externí odkaz:
http://arxiv.org/abs/1905.00266
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-8 (2023)
Abstract Prediction of the spaces used by animals is an important component of wildlife management, but requires detailed information such as animal visit and occupy in a short span of the target species. Computational simulation is often employed as
Externí odkaz:
https://doaj.org/article/980f1cb8779a487e9172fa6db9bf7965
Akademický článek
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The objective of this study is to investigate spatial structures of error in the assessment of continuous raster data. The use of conventional diagnostics of error often overlooks the possible spatial variation in error because such diagnostics repor
Externí odkaz:
http://arxiv.org/abs/1810.00514