Meta-Learning: Teaching Machines How to Learn More Efficiently

Abstract

Meta-learning research is still being conducted in a number of ways. The identification of meta-features is one area. The field of meta-learning, often known as learning-to-learn, has seen a dramatic surge in interest in recent years. While conventional AI approaches aim to solve issues by integrating data from many learning events into an existing learning algorithm, meta-learning takes the opposite tack by attempting to enhance the learning algorithm. In order to encourage researchers to investigate interdisciplinary links and advance meta-learning while combining it with other AI research disciplines, we want to evaluate the present status of the field and highlight its prospects and limitations. By working together, we can overcome current obstacles and fully use meta-learning to handle a wide range of intricate issues that intelligent systems encounter. Still, further study in this field can be done to better understand how meta-learning techniques may be used to continuously modify, rebuild, or eliminate base-learners.