| 書目名稱 | From Unimodal to Multimodal Machine Learning |
| 副標題 | An Overview |
| 編輯 | Bla? ?krlj |
| 視頻video | http://file.papertrans.cn/349/348995/348995.mp4 |
| 概述 | Focuses on combining internal representations in multimodal machine learning.Explores different approaches to solving the challenge of combining information.Includes an overview of the trends in the f |
| 叢書名稱 | SpringerBriefs in Computer Science |
| 圖書封面 |  |
| 描述 | .With the increasing amount of various data types, machine learning methods capable of leveraging diverse sources of information have become highly relevant. Deep learning-based approaches have made significant progress in learning from texts and images in recent years. These methods enable simultaneous learning from different types of representations (embeddings). Substantial advancements have also been made in joint learning from different types of spaces. Additionally, other modalities such as sound, physical signals from the environment, and time series-based data have been recently explored. Multimodal machine learning, which involves processing?and learning from data across multiple modalities, has opened up new possibilities?in a wide range of applications, including speech recognition, natural language?processing, and image recognition..From Unimodal to Multimodal Machine Learning: An Overview.?gradually introduces the concept of multimodal machine learning, providing readers with the necessary background to understand this type of learning and its implications. Key methods representative of different modalities are described in more detail, aiming to offer an understanding |
| 出版日期 | Book 2024 |
| 關(guān)鍵詞 | Machine learning; data mining; unimodal machine learning; multimodal machine learning; algorithms; langua |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-031-57016-2 |
| isbn_softcover | 978-3-031-57015-5 |
| isbn_ebook | 978-3-031-57016-2Series ISSN 2191-5768 Series E-ISSN 2191-5776 |
| issn_series | 2191-5768 |
| copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |
|Archiver|手機版|小黑屋|
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