| ISBN: | 9783030046620 |
| Publication date: | 5th December 2018 |
| Author: | Sarah Vluymans |
| Publisher: | Springer an imprint of Springer International Publishing |
| Format: | Hardback |
| Pagination: | 249 pages |
| Series: | Studies in Computational Intelligence |
| Genres: |
Artificial intelligence |
This book presents novel classification algorithms for four challenging prediction tasks, namely learning from imbalanced, semi-supervised, multi-instance and multi-label data. The methods are based on fuzzy rough set theory, a mathematical framework used to model uncertainty in data.
Dealing With Imbalanced and Weakly Labelled Data in Machine Learning Using Fuzzy and Rough Set Methods features in the following genres: Artificial intelligence
Hardback. Not Available.
Dealing With Imbalanced and Weakly Labelled Data in Machine Learning Using Fuzzy and Rough Set Methods was written by Sarah Vluymans and published by Springer an imprint of Springer International Publishing
Dealing With Imbalanced and Weakly Labelled Data in Machine Learning Using Fuzzy and Rough Set Methods has 249 pages
Yes it is part of Studies in Computational Intelligence series