  Machine learning (ML) has emerged as a prominent field of research in
computer science and other related fields, thereby driving advancements in
other domains of interest. As the field continues to evolve, it is crucial to
understand the landscape of highly cited publications to identify key trends,
influential authors, and significant contributions made thus far. In this
paper, we present a comprehensive bibliometric analysis of highly cited ML
publications. We collected a dataset consisting of the top-cited papers from
reputable ML conferences and journals, covering a period of several years from
1959 to 2022. We employed various bibliometric techniques to analyze the data,
including citation analysis, co-authorship analysis, keyword analysis, and
publication trends. Our findings reveal the most influential papers, highly
cited authors, and collaborative networks within the machine learning
community. We identify popular research themes and uncover emerging topics that
have recently gained significant attention. Furthermore, we examine the
geographical distribution of highly cited publications, highlighting the
dominance of certain countries in ML research. By shedding light on the
landscape of highly cited ML publications, our study provides valuable insights
for researchers, policymakers, and practitioners seeking to understand the key
developments and trends in this rapidly evolving field.
