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Machine learning (ML) has emerged as a prominent field of research incomputer science and other related fields, thereby driving advancements inother domains of interest. As the field continues to evolve, it is crucial tounderstand the landscape of highly cited publications to identify key trends,influential authors, and significant contributions made thus far. In thispaper, we present a comprehensive bibliometric analysis of highly cited MLpublications. We collected a dataset consisting of the top-cited papers fromreputable ML conferences and journals, covering a period of several years from1959 to 2022. We employed various bibliometric techniques to analyze the data,including citation analysis, co-authorship analysis, keyword analysis, andpublication trends. Our findings reveal the most influential papers, highlycited authors, and collaborative networks within the machine learningcommunity. We identify popular research themes and uncover emerging topics thathave recently gained significant attention. Furthermore, we examine thegeographical distribution of highly cited publications, highlighting thedominance of certain countries in ML research. By shedding light on thelandscape of highly cited ML publications, our study provides valuable insightsfor researchers, policymakers, and practitioners seeking to understand the keydevelopments and trends in this rapidly evolving field.

A Comprehensive Study of Groundbreaking Machine Learning Research: Analyzing highly cited and impactful publications across six decades