Multimodal Machine Learning: Techniques and Applications explains recent advances in multimodal machine learning, providing a coherent set of fundamentals for designing efficient multimodal learning algorithms for different applications. The book addresses the main challenges in multimodal machine learning based computing paradigms, including multimodal representation learning, translation and mapping, modality alignment, multimodal fusion and co-learning. The book also explores the important texture feature descriptors based on recognition and transform techniques. It is ideal for senior undergraduates, graduate students, and researchers in data science, engineering, computer science and statistics.
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1. Introduction 2. Co-Learning and Fusion based learning Techniques 3. Multimodal representation and descriptive System 4. Joint Multimodal Representations 5. Representation and Translation 6. Alignment and Fusion Methods 7. Multimodal Machine Learning Techniques and framework for Biometric-based System 8. Emerging Trends and Future Challenges
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Product details
ISBN
9780128237373
Published
2021-05-01
Publisher
Elsevier Science Publishing Co Inc
Height
235 mm
Width
191 mm
Age
06, P
Language
Product language
Engelsk
Format
Product format
Heftet
Number of pages
375