Résumé
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.
| langue originale | Anglais |
|---|---|
| titre | ESANN 2016 |
| Sous-titre | Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning |
| Editeur | i6doc.com publication |
| Pages | 623-628 |
| Nombre de pages | 6 |
| ISBN (Electronique) | 9782875870278 |
| ISBN (imprimé) | 978-287587027-8 |
| Etat de la publication | Publié - 29 avr. 2016 |
| Evénement | 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2016 - Bruges, Belgique Durée: 27 avr. 2016 → 29 avr. 2016 |
Une conférence
| Une conférence | 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2016 |
|---|---|
| Pays/Territoire | Belgique |
| La ville | Bruges |
| période | 27/04/16 → 29/04/16 |