Asit Barman;Swalpa Kumar Roy;Swagatam Das;Paramartha Dutta
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引用次数: 0
Abstract
In the vast landscape of machine learning, meta-learning stands out as a challenging and dynamic area of exploration. While traditional machine learning models rely on standard algorithms to learn from data, meta-learning elevates this process by leveraging prior knowledge to adapt and improve learning experiences, mimicking the adaptive nature of human learning. This paradigm offers promising avenues for addressing the limitations of conventional deep learning approaches, such as data and computational constraints, as well as issues related to generalization. In this comprehensive survey, we delve into the intricacies of meta-learning methodologies. Beginning with a foundational overview of meta-learning and its associated fields, we present a detailed methodology elucidating the workings of meta-learning. Recognizing the importance of rigorous evaluation, we also furnish a comprehensive summary of prevalent benchmark datasets and recent advancements in meta-learning techniques applied to these datasets. Additionally, we explore meta-learning's diverse applications and achievements in domains like reinforcement learning and few-shot learning. Lastly, we examine practical hurdles and potential research directions, providing insights for future endeavors in this burgeoning field.
期刊介绍:
The IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI) publishes original articles on emerging aspects of computational intelligence, including theory, applications, and surveys.
TETCI is an electronics only publication. TETCI publishes six issues per year.
Authors are encouraged to submit manuscripts in any emerging topic in computational intelligence, especially nature-inspired computing topics not covered by other IEEE Computational Intelligence Society journals. A few such illustrative examples are glial cell networks, computational neuroscience, Brain Computer Interface, ambient intelligence, non-fuzzy computing with words, artificial life, cultural learning, artificial endocrine networks, social reasoning, artificial hormone networks, computational intelligence for the IoT and Smart-X technologies.