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 language | English |
|---|---|
| Title of host publication | ESANN 2016 |
| Subtitle of host publication | Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning |
| Publisher | i6doc.com publication |
| Pages | 623-628 |
| Number of pages | 6 |
| ISBN (Electronic) | 9782875870278 |
| ISBN (Print) | 978-287587027-8 |
| Publication status | Published - 29 Apr 2016 |
| Event | 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2016 - Bruges, Belgium Duration: 27 Apr 2016 → 29 Apr 2016 |
Conference
| Conference | 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2016 |
|---|---|
| Country | Belgium |
| City | Bruges |
| Period | 27/04/16 → 29/04/16 |
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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