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Bridging Information Visualization with Machine Learning (Dagstuhl Seminar 15101)

descriptionPublicationkeyboard_double_arrow_right Article 01 Jan 2015 English Publisher:Schloss Dagstuhl – Leibniz-Zentrum für Informatik
Authors: Keim, Daniel A.; Munzner, Tamara; Rossi, Fabrice; Verleysen, Michael;

Bridging Information Visualization with Machine Learning (Dagstuhl Seminar 15101)

Abstract

This report documents the program and the outcomes of Dagstuhl Seminar 15101 "Bridging Information Visualization with Machine Learning". This seminar is a successor to Dagstuhl seminar 12081 "Information Visualization, Visual Data Mining and Machine Learning" held in 2012. The main goal of this second seminar was to identify important challenges to overcome in order to build systems that integrate machine learning and information visualization.

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Keywords

Exploratory data analysis, Information visualization, Visual data mining, Machine learning, info:eu-repo/classification/ddc/004, 004, ddc: ddc:004

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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