Visual Analysis of Public Utility Service Problems in a Metropolis

Zhang, J.; Yanli, E.; Ma, Jing; Zhao, Yahui; Xu, Binghan; Sun, Liting; Chen, Jinyan; Yuan, Xiaoru

doi:10.1109/tvcg.2014.2346898

Cited by 34 publications

(12 citation statements)

References 37 publications

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“…The tool also reveals how these parameters are related to each other in the prediction model. Zhang et al [266] proposed a visual analytics method to visualize how variables affect statistical indicators in a logistic regression model.…”

Section: Understanding the Effects Of Parametersmentioning

confidence: 99%

A survey of visual analytics techniques for machine learning

et al. 2020

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Visual analytics for machine learning has recently evolved as one of the most exciting areas in the field of visualization. To better identify which research topics are promising and to learn how to apply relevant techniques in visual analytics, we systematically review 259 papers published in the last ten years together with representative works before 2010. We build a taxonomy, which includes three first-level categories: techniques before model building, techniques during modeling building, and techniques after model building. Each category is further characterized by representative analysis tasks, and each task is exemplified by a set of recent influential works. We also discuss and highlight research challenges and promising potential future research opportunities useful for visual analytics researchers.

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Section: Understanding the Effects Of Parametersmentioning

confidence: 99%

A survey of visual analytics techniques for machine learning

et al. 2020

View full text Add to dashboard Cite

show abstract

“…The tool also reveals how these parameters are related to each other in the prediction model. Zhang et al [293] proposed a visual analytics method to visualize how variables affect statistical indicators in a logistic regression model.…”

Section: Understanding the Effects Of Parametersmentioning

confidence: 99%

A Survey of Visual Analytics Techniques for Machine Learning

Yuan

Chen

Yang

et al. 2020

Preprint

View full text Add to dashboard Cite

Visual analytics for machine learning has recently evolved as one of the most exciting areas in the field of visualization. To better identify which research topics are promising and to learn how to apply relevant techniques in visual analytics, we systematically review 259 papers published in the last ten years together with representative works before 2010. We build a taxonomy, which includes three first-level categories: techniques before model building, techniques during model building, and techniques after model building. Each category is further characterized by representative analysis tasks, and each task is exemplified by a set of recent influential works. We also discuss and highlight research challenges and promising potential future research opportunities useful for visual analytics researchers.

show abstract

“…Spatial data is extensively received from various sources and metadata is used to give information about spatial dataset. Metadata is represented and transformed in electronic form in digital archives such as metadata information system [4]. Following issues are related to spatial data: 1) Entry of spatial data and metadata.…”

Section: Introductionmentioning

confidence: 99%