Multimodal Learning Using Heterogeneous (13 results)
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Multimodal Learning Using Heterogeneous Data
Eslamian, Saeid (EDT); Nanjundan, Preethi (EDT); George, Jossy P. (EDT); Eslamian, Faezeh (EDT)
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Multimodal Learning Using Heterogeneous Data
Eslamian, Saeid (EDT); Nanjundan, Preethi (EDT); George, Jossy P. (EDT); Eslamian, Faezeh (EDT)
- Softcover
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Multimodal Learning Using Heterogeneous Data
Eslamian, Saeid (EDT); Nanjundan, Preethi (EDT); George, Jossy P. (EDT); Eslamian, Faezeh (EDT)
- Softcover
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Multimodal Learning Using Heterogeneous Data
Eslamian, Saeid (EDT); Nanjundan, Preethi (EDT); George, Jossy P. (EDT); Eslamian, Faezeh (EDT)
- Softcover
Seller: GreatBookPricesUK, Woodford Green, United KingdomGreatBookPricesUK
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- Softcover
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Condition: New. Provides a detailed exploration of multimodal learning techniques with a special focus on handling heterogeneous data sourcesDelves into advanced techniques such as deep fusion, graph-based methods, and attention mechanisms, catering to rea.
Language: English
Published by Elsevier Science & Technology, Morgan Kaufmann Dez 2025, 2025
- Softcover
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Taschenbuch. Condition: Neu. Neuware - Multimodal Learning Using Heterogeneous Data is a comprehensive guide to the emerging field of multimodal learning, which focuses on integrating diverse data types such as text, images, and audio within a unified framework. The book delves into the challenges and opportunities presented by…multimodal data and offers insights into the foundations, techniques, and applications of this interdisciplinary approach. It is intended for researchers and practitioners interested in learning more about multimodal learning and is a valuable resource for those working on projects involving data analysis from multiple modalities.The book begins with a comprehensive introduction, focusing on multimodal learning's foundational principles and the intricacies of heterogeneous data. It then delves into feature extraction, fusion techniques, and deep learning architectures tailored for multimodal data. It also covers transfer learning, pre-processing challenges, and cross-modal information retrieval. The book highlights the application of multimodal learning in specialized contexts such as sentiment analysis, data generation, medical imaging, and ethical considerations. Real-world case studies are woven into the narrative, illuminating the applications of multimodal learning in diverse domains such as natural language processing, multimedia content analysis, autonomous systems, and cognitive computing. The book concludes with an insightful exploration of multimodal data analytics across social media, surveillance, user behavior, and a forward-looking examination of future trends and practical implementations. As a collective resource, Multimodal Learning Using Heterogeneous Data illuminates the powerful utility of multimodal learning to elevate machine learning tasks while also highlighting the need for innovative solutions and methodologies. The book acknowledges the challenges associated with deep learning and the growing importance of ethical considerations in the collection and analysis of multimodal data.Overall, Multimodal Learning Using Heterogeneous Data provides an expansive panorama of this rapidly evolving field, its potential for future research and application, and its vital role in shaping machine learning's evolution.
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Seller: Brook Bookstore On Demand, Napoli, NA, ItalyBrook Bookstore On Demand
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Condition: new. Questo è un articolo print on demand.
- Softcover
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Seller: Revaluation Books, Exeter, United KingdomRevaluation Books
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Paperback. Condition: Brand New. 250 pages. 9.25x7.50x1.73 inches. In Stock. This item is printed on demand.
Language: English
Published by Elsevier Science & Technology, San Francisco, 2025
- Softcover
- Print on Demand
Seller: CitiRetail, Stevenage, United KingdomCitiRetail
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Paperback. Condition: new. Paperback. Multimodal Learning Using Heterogeneous Data This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
