株式会社極東書店トップ ＞ 商品一覧 ＞ Bayesian Tensor Decomposition for Signal Processing and Machine Learning: Modeling, Tuning-Free Algorithms, and Applications. 2023 ed.
商品詳細
Bayesian Tensor Decomposition for Signal Processing and Machine Learning: Modeling, Tuning-Free Algorithms, and Applications. 2023 ed.
・ISBN 978-3-031-22437-9 hard EUR 129.99
￥34,745.- (税込) ※（※）価格はご注文時の参考価格となります。
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お気に入り
★★★
| 著者・編者 | Cheng, Lei / Chen, Zhongtao / Wu, Yik-Chung, |
|---|---|
| 出版社 | (Springer International Publishing AG, SZ) |
| 出版年月 | 2023 |
| ページ数 | 183 pp. |
| 言語 | ENG |
| ニュース番号 | ＜A02-99973＞ |
解説
This book presents recent advances of Bayesian inference in structured tensor decompositions. It explains how Bayesian modeling and inference lead to tuning-free tensor decomposition algorithms, which achieve state-of-the-art performances in many applications, including
The book begins with an introduction to the general topics of tensors and Bayesian theories. It then discusses probabilistic models of various structured tensor decompositions and their inference algorithms, with applications tailored for each tensor decomposition presented in the corresponding chapters. The book concludes by looking to the future, and areas where this research can be further developed.
Bayesian Tensor Decomposition for Signal Processing and Machine Learning is suitable for postgraduates and researchers with interests in tensor data analytics and Bayesian methods.
- blind source separation;
- social network mining;
- image and video processing;
- array signal processing; and,
- wireless communications.
The book begins with an introduction to the general topics of tensors and Bayesian theories. It then discusses probabilistic models of various structured tensor decompositions and their inference algorithms, with applications tailored for each tensor decomposition presented in the corresponding chapters. The book concludes by looking to the future, and areas where this research can be further developed.
Bayesian Tensor Decomposition for Signal Processing and Machine Learning is suitable for postgraduates and researchers with interests in tensor data analytics and Bayesian methods.