Information Visualisation and Machine Learning: Characteristics, Convergence and Perspective

Research output: Contribution in Book/Catalog/Report/Conference proceedingConference contribution

Abstract

This paper discusses how information visualisation and machine learning can cross-fertilise. On the one hand, the user-centric field of information visualisation can help machine learning to better integrate users in the learning, assessment and interpretation processes. On the other hand, machine learning can provide powerful algorithms for clustering, dimensionality reduction, data cleansing, outlier detection, etc. Such inference tools are required to create efficient visualisations. This paper highlight opportunities to collaborate for experts in both fields.

Original languageEnglish
Title of host publicationESANN 2016
Subtitle of host publicationProceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Publisheri6doc.com publication
Pages623-628
Number of pages6
ISBN (Electronic)9782875870278
ISBN (Print)978-287587027-8
Publication statusPublished - 29 Apr 2016
Event24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2016 - Bruges, Belgium
Duration: 27 Apr 201629 Apr 2016

Conference

Conference24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2016
CountryBelgium
CityBruges
Period27/04/1629/04/16

Fingerprint Dive into the research topics of 'Information Visualisation and Machine Learning: Characteristics, Convergence and Perspective'. Together they form a unique fingerprint.

  • Cite this

    Frenay, B., & Dumas, B. (2016). Information Visualisation and Machine Learning: Characteristics, Convergence and Perspective. In ESANN 2016: Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (pp. 623-628). i6doc.com publication. https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2016-18.pdf