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.

通过对高被引机器学习论文进行综合评析，我们揭示了机器学习领域内最有影响力的论文、高被引作者以及合作网络。我们还发现了热门研究主题和最新受到关注的新兴话题，并对高被引论文的地理分布进行了分析，突显了某些国家在机器学习研究中的主导地位。通过为研究人员、政策制定者和实践者提供关于这一快速发展领域的主要进展和趋势的有价值的见解，我们的研究为它们提供了有价值的洞察。