| 书目名称 | Information Fusion | | 副标题 | Machine Learning Met | | 编辑 | Jinxing Li,Bob Zhang,David Zhang | | 视频video | | | 概述 | Reviews state-of-the-art techniques for information fusion.Presents typical applications of information fusion, ranging from low-level to high-level tasks.Demonstrates the benefits of applying advance | | 图书封面 |  | | 描述 | .In the big data era, increasing information can be extracted from the same source object or scene. For instance, a person can be verified based on their fingerprint, palm print, or iris information, and a given image can be represented by various types of features, including its texture, color, shape, etc. These multiple types of data extracted from a single object are called multi-view, multi-modal or multi-feature data. Many works have demonstrated that the utilization of all available information at multiple abstraction levels (measurements, features, decisions) helps to obtain more complex, reliable and accurate information and to maximize performance in a range of applications..This book provides an overview of information fusion technologies, state-of-the-art techniques and their applications. It covers a variety of essential information fusion methods based on different techniques, including sparse/collaborative representation, kernel strategy,Bayesian models, metric learning, weight/classifier methods, and deep learning. The typical applications of these proposed fusion approaches are also presented, including image classification, domain adaptation, disease detection, ima | | 出版日期 | Book 2022 | | 关键词 | Information Fusion; Data Fusion; Multi-view data; Multi-modal data; Multi-feature data; Multi-view Learni | | 版次 | 1 | | doi | https://doi.org/10.1007/978-981-16-8976-5 | | isbn_softcover | 978-981-16-8978-9 | | isbn_ebook | 978-981-16-8976-5 | | copyright | Springer Nature Singapore Pte Ltd. & Higher Education Press, China 2022 |
The information of publication is updating
|
|
发表于 2025-3-21 17:45:10
