Multimodal Machine Learning: Techniques and Applications

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. Presents new representation, classification and identification algorithms for data prediction and analysis on feature characteristicsDiscusses recent and future advancements in diversified fields of computer vision , pattern recognition, generative adversarial network-based learning, video analytics and data scienceProvides an overview of future research challenges and directions INDICE: 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

• ISBN: 978-0-12-823737-3
• Editorial: Academic Press
• Encuadernacion: Rústica
• Páginas: 375
• Fecha Publicación: 04/01/2021
• Nº Volúmenes: 1
• Idioma: Inglés