Machine learning is a very active sub-field of artificial intelligence concerned with the development of computational models of learning. From a computational point of view, machine learning refers to the ability of a machine to improve its performance based on previous results. From a biological point of view, machine learning is the study of how to create computers that will learn from experience and modify their activity based on that learning as opposed to traditional computers whose activity will not change unless the programmer explicitly changes it. Currently, learning systems have been becoming large, heterogeneous, uncertain, and dynamic. They also need to match some conflict requirements, such as flexibility, performance, resource usage, and reliability. In this special issue, the researchers will pay more attention to the top trends in machine learning, and discuss how to take full advantage of the benefits of machine learning trends in practical applications.
Topics/Areas
• Adversarial and Trustworthy Machine Learning.
• Automated Machine Learning.
• Bias Removal in Machine Learning.
• Evolutionary Machine Learning.
• Generative Adversarial Networks.
• Improved Machine Learning with ChatGPT.
• Machine Learning for Dynamical Systems.
• Machine Learning with Multiple Objectives.
• Multi-Model Learning and Ensemble Learning Algorithms.
• Real-Time Machine Learning.
• Small Data and Tiny Machine Learning.
• Synthetic Data for Better Machine Learning.
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