Name
Abstract | ||
|---|---|---|
Which topics of machine learning are most commonly addressed in research? This question was initially answered in 2007 by doing a qualitative survey among distinguished researchers. In our study, we revisit this question from a quantitative perspective. Concretely, we collect 54K abstracts of papers published between 2007 and 2016 in leading machine learning journals and conferences. We then use machine learning in order to determine the top 10 topics in machine learning. We not only include models, but provide a holistic view across optimization, data, features, etc. This quantitative approach allows reducing the bias of surveys. It reveals new and up-to-date insights into what the 10 most prolific topics in machine learning research are. This allows researchers to identify popular topics as well as new and rising topics for their research. |
Year | Venue | DocType |
|---|---|---|
2017 | ESANN | Conference |
Volume | ISSN | Citations |
abs/1703.10121 | Proceedings of the 25th European Symposium on Artificial Neural
Networks, Computational Intelligence and Machine Learning (ESANN 2017) | 0 |
PageRank | References | Authors |
0.34 | 2 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Patrick O. Glauner | 1 | 26 | 5.75 |
| Manxing Du | 2 | 1 | 0.70 |
| Victor Paraschiv | 3 | 0 | 0.34 |
| Andrey Boytsov | 4 | 0 | 0.34 |
| Isabel Lopez Andrade | 5 | 0 | 0.34 |
| Jorge Augusto Meira | 6 | 18 | 5.77 |
| Petko Valtchev | 7 | 902 | 72.38 |
| Radu State | 8 | 623 | 86.87 |