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huskyHusky malamute-camerafan

Husky malamute-camerafan

Pat Bleu

 

Sensotrendin esitys ensimmäistä kertaa järjestetyillä Diabetesmessuilla. Itse kalvot ovat lähinnä kuvia, mutta muistiinpanoista näkyy myös tarinaa (helpompi seurata, jos lataat PowerPoint-tiedoston).Sensotrend Diabetesmessuilla

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Mikael Rinnetmäki

 

Resume - Ganesh Ramakrishnan

Resume - Ganesh Ramakrishnan

Ramakrishnan Ganesh

 

Geographically Weighted Regression (GWR) is a local version of spatial regression that captures spatial dependency in regression analysis. GWR has many application in practice as a visualization and prediction tool for spatial exploration- (e.g in climate, economy, medical). However, this locally regression model is slow in process upon the volume of calculations and the spatial getting bigger. Improving performance of GWR is an critical issue, but their distributed implementations have not been studied. Recently, with the advent of Spark as well MapReduce framework, the development of machine learning applications and parallel programming becomes easier. In this article, we propose several large-scale implementations of distributed GWR, leveraging Spark framework. We implemented and evaluated these approaches with large datasets. To our best knowledge, this is the first work addressing GWR at large-scale.Large-Scale Geographically Weighted Regression on Spark

Large-Scale Geographically Weighted Regression on Spark

Viet-Trung TRAN

 

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Logos Academy

 

Ficha 9 - formación humana 2016Ficha 9 - formación humana 2016

Ficha 9 - formación humana 2016

Logos Academy

 

Destacado (6)

Husky malamute-camerafan

Husky malamute-camerafan

 

Sensotrend Diabetesmessuilla

Sensotrend Diabetesmessuilla

 

Resume - Ganesh Ramakrishnan

Resume - Ganesh Ramakrishnan

 

Large-Scale Geographically Weighted Regression on Spark

Large-Scale Geographically Weighted Regression on Spark

 

Contar

Contar

 

Ficha 9 - formación humana 2016

Ficha 9 - formación humana 2016

 

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