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.

翻译：机器学习（ML）已成为计算机科学及相关领域的重要研究学科，并推动着其他领域的进步。随着该领域的持续演进，厘清高被引出版物的研究图景，对于识别关键趋势、核心作者与重要贡献至关重要。本文对高被引机器学习出版物进行了全面的文献计量分析。我们构建了一个包含1959至2022年间顶级ML会议与期刊高被引论文的数据集，并综合运用引文分析、合著分析、关键词分析及出版趋势分析等文献计量方法。研究结果揭示了机器学习领域最具影响力的论文、高被引作者及合作网络，识别出流行研究主题，并发现了近期备受关注的新兴方向。此外，我们还考察了高被引出版物的地理分布特征，凸显了部分国家在ML研究中的主导地位。通过揭示高被引ML出版物的整体图景，本研究为研究者、政策制定者及从业者理解这一快速发展领域的关键进展与趋势提供了重要参考。