| 书目名称 | Machine Learning for Astrophysics |
| 副标题 | Proceedings of the M |
| 编辑 | Filomena Bufano,Simone Riggi,Francesco Schilliro |
| 视频video | |
| 概述 | Provides a comprehensive view of machine learning techniques applied to astrophysics.Discusses limitations of ML applications to astrophysics.With a feature on how to face future radioastronomy data d |
| 丛书名称 | Astrophysics and Space Science Proceedings |
| 图书封面 |  |
| 描述 | .This book reviews the state of the art in the exploitation of machine learning techniques for the astrophysics community and gives the reader a complete overview of the field. The contributed chapters allow the reader to easily digest the material through balanced theoretical and numerical methods and tools with applications in different fields of theoretical and observational astronomy. The book helps the reader to really understand and quantify both the opportunities and limitations of using machine learning in several fields of astrophysics.. |
| 出版日期 | Conference proceedings 2023 |
| 关键词 | time series in astronomy and astrophysics; anomaly discovery in data; machine learning techniques; soft |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-031-34167-0 |
| isbn_softcover | 978-3-031-34169-4 |
| isbn_ebook | 978-3-031-34167-0Series ISSN 1570-6591 Series E-ISSN 1570-6605 |
| issn_series | 1570-6591 |
| copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |
发表于 2025-3-21 19:10:03
