Automated Machine Learning and Meta-Learning for Multimedia

This book summarizes the recent research progress and frontier development on AutoML and meta-learning,and their applications in computer vision,natural language processing,multimedia,and data mining related fields. It is accessible to the whole machine learning community and designed for introductory and intermediate audiences.

Format: Paperback / softback
Length: 224 pages
Publication date: 25 November 2022
Publisher: Springer Nature Switzerland AG

This comprehensive book serves as a valuable resource for disseminating and advancing the latest research developments and cutting-edge advancements in AutoML and meta-learning, with a particular focus on their applications in computer vision, natural language processing, multimedia, and data mining. These emerging and rapidly evolving research directions in the broader realm of machine learning hold immense excitement and promise. The authors passionately advocate for novel, high-quality research findings and innovative solutions to the complex challenges encountered in AutoML and meta-learning. This topic, at the core of artificial intelligence, attracts a diverse audience from both academia and industry. The book's accessibility is enhanced by its clear and concise writing style, making it suitable for a wide range of machine learning enthusiasts, including researchers, students, and practitioners interested in AutoML, meta-learning, and their applications in multimedia, computer vision, natural language processing, and data mining tasks. Whether you are a novice or an experienced professional, this book offers valuable insights and practical knowledge that will help you deepen your understanding of these topics and apply them effectively in your work.

Introduction:
AutoML (Automated Machine Learning) and meta-learning have emerged as key paradigms in the field of machine learning, revolutionizing the way we develop and deploy intelligent systems. These approaches automate the process of building machine learning models, enabling researchers and practitioners to focus on solving complex problems and exploring new research directions. In this book, we will delve into the latest research progress and frontier development in AutoML and meta-learning, as well as their applications in various computer vision, natural language processing, multimedia, and data mining related fields.
Chapter 1:
AutoML and Meta-Learning: An Overview
In this chapter, we will provide a comprehensive introduction to AutoML and meta-learning, their theoretical foundations, and their practical applications. We will discuss the challenges faced in traditional machine learning and how AutoML and meta-learning address them. We will also explore the key concepts and algorithms involved in AutoML and meta-learning, such as feature selection, model selection, and hyperparameter tuning.
Chapter 2:
Recent Research Progress in AutoML
In this chapter, we will showcase the latest research developments in AutoML, including algorithm improvements, model optimization techniques, and application-specific adaptations. We will discuss the state-of-the-art AutoML frameworks and tools available in the literature, and their performance in various machine learning tasks. We will also highlight the open-source projects and communities that contribute to the growth and development of AutoML.
Chapter 3:
Meta-Learning: A Versatile Approach
Meta-learning is a powerful technique that enables the learning of transferable knowledge and skills across different domains and tasks. In this chapter, we will explore the theoretical foundations of meta-learning, including reinforcement learning, transfer learning, and meta-optimization. We will discuss the challenges and opportunities associated with meta-learning, and the various approaches and algorithms used to address them. We will also showcase the applications of meta-learning in computer vision, natural language processing, multimedia, and data mining related fields.
Chapter 4:
Applications of AutoML and Meta-Learning in Computer Vision
Computer vision is a rapidly growing field that involves the analysis and interpretation of visual data. In this chapter, we will discuss the applications of AutoML and meta-learning in computer vision, including object detection, image classification, and scene understanding. We will explore the state-of-the-art techniques and models used in computer vision, and how AutoML and meta-learning can enhance their performance and efficiency. We will also highlight the open-source projects and datasets that contribute to the development of computer vision applications.
Chapter 5:
Applications of AutoML and Meta-Learning in Natural Language Processing
Natural language processing (NLP) is a critical component of intelligent systems that enables human-computer interaction and the understanding of human language. In this chapter, we will discuss the applications of AutoML and meta-learning in NLP, including text classification, sentiment analysis, and language translation. We will explore the state-of-the-art techniques and models used in NLP, and how AutoML and meta-learning can improve their accuracy and efficiency. We will also highlight the open-source projects and datasets that contribute to the development of NLP applications.
Chapter 6:
Applications of AutoML and Meta-Learning in Multimedia
Multimedia is a rich source of data that can be used for various applications, such as video analysis, image processing, and audio recognition. In this chapter, we will discuss the applications of AutoML and meta-learning in multimedia, including content generation, video summarization, and image restoration. We will explore the state-of-the-art techniques and models used in multimedia, and how AutoML and meta-learning can enhance their performance and efficiency. We will also highlight the open-source projects and datasets that contribute to the development of multimedia applications.
Chapter 7:
Applications of AutoML and Meta-Learning in Data Mining
Data mining is a process of extracting valuable insights and patterns from large datasets. In this chapter, we will discuss the applications of AutoML and meta-learning in data mining, including anomaly detection, pattern recognition, and association rule

Weight: 391g
Dimension: 235 x 155 (mm)
ISBN-13: 9783030881344
Edition number: 1st ed. 2021