株式会社極東書店トップ ＞ 商品一覧 ＞ Mathematical Modeling for Data Science : N2ADS, Athens, Greece, April, 7-8, 2025 and M2A25, Marrakech, Morocco, February, 18-20, 2025.
商品詳細
Mathematical Modeling for Data Science : N2ADS, Athens, Greece, April, 7-8, 2025 and M2A25, Marrakech, Morocco, February, 18-20, 2025.
・ISBN 978-3-032-20504-9 hard EUR 199.99
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| 著者・編者 | Bentbib, Abdesslem / Jbilou, Khalide / Mitrouli, Marilena (eds.), |
|---|---|
| シリーズ | Springer Proceedings in Mathematics & Statistics |
| 出版社 | (Springer Nature Switzerland AG, SZ) |
| 出版年月 | 2026 |
| ページ数 | 382 pp. |
| 言語 | ENG |
| ニュース番号 | ＜M25-21984＞ |
解説
This book presents selected chapters presented in two international conferences where more than 20 countries are represented by more than 100 participants, consisting of academic researchers and scientists working in industry. The conferences cover different areas related to the application of numerical analysis to practical problems in engineering, industry, environment, medical imaging, and new image and information technologies. Participants shared their recent contributions and their experience in different fields. The plenary speakers are very well known and most of them are editors or editors in chief of prestigious international journals (L. Reichel, Y. Saad, D. Szyld, H. Sadok, S. Serra-Capizzano....)
Description of the volume-The subject of the book is to present selected papers, using two referees for each paper, that were presented during the two conference m2a25 held in Marrakech on February 2025 and n2ads held in Athens on April 2025. The accepted papers were in the topics of the conferences with special applications to data science.
The topics of interested were
* Completion Methods and Applications to Data Science.
* Inverse-Ill-posed Problems, Optimization.
* Applied Statistics, Applications to Engineering, Biodiversity, Imaging, Big Data, Machine Learning,
This book offers numerous benefits to future readers, particularly those interested in data driven. Readers gain a deep understanding of the mathematical principles that under data science techniques. This foundation help them not only use algorithms effectively but also understand why and how these algorithms work.