2024.0051ABSTRACTMachine learning(ML)has transformed numerous fields,but understanding its foundational research is crucial for its continued progress.This paper presents an overview of the major classical ML algorith...2024.0051ABSTRACTMachine learning(ML)has transformed numerous fields,but understanding its foundational research is crucial for its continued progress.This paper presents an overview of the major classical ML algorithms and examines the state-of-the-art publications,spanning seventy decades,through an extensive bibliometric analysis.We analyzed a dataset of highly cited papers from prominent ML conferences and journals,employing techniques such as citation and keyword analyses to uncover key insights.The study further identifies the most influential papers and authors,reveals the evolving collaborative networks within the ML community,and pinpoints prevailing research themes and emerging areas of focus.Additionally,we examine the geographic distribution of highly cited publications,highlighting the leading countries in ML research.This study provides a comprehensive overview of the evolution of traditional learning algorithms,and their impacts and discusses challenges and opportunities for future development,with a particular focus on the Global South.The findings from this paper offer valuable insights for both ML experts and the broader research community,enhancing understanding of the field’s trajectory and its significant influence on recent advances in learning algorithms.展开更多
文摘2024.0051ABSTRACTMachine learning(ML)has transformed numerous fields,but understanding its foundational research is crucial for its continued progress.This paper presents an overview of the major classical ML algorithms and examines the state-of-the-art publications,spanning seventy decades,through an extensive bibliometric analysis.We analyzed a dataset of highly cited papers from prominent ML conferences and journals,employing techniques such as citation and keyword analyses to uncover key insights.The study further identifies the most influential papers and authors,reveals the evolving collaborative networks within the ML community,and pinpoints prevailing research themes and emerging areas of focus.Additionally,we examine the geographic distribution of highly cited publications,highlighting the leading countries in ML research.This study provides a comprehensive overview of the evolution of traditional learning algorithms,and their impacts and discusses challenges and opportunities for future development,with a particular focus on the Global South.The findings from this paper offer valuable insights for both ML experts and the broader research community,enhancing understanding of the field’s trajectory and its significant influence on recent advances in learning algorithms.
