Summary of Classical Machine Learning: Seventy Years Of Algorithmic Learning Evolution, by Absalom E. Ezugwu et al.
Classical Machine Learning: Seventy Years of Algorithmic Learning Evolution
by Absalom E. Ezugwu, Yuh-Shan Ho, Ojonukpe S. Egwuche, Olufisayo S. Ekundayo, Annette Van Der Merwe, Apu K. Saha, Jayanta Pal
First submitted to arxiv on: 3 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Machine learning has revolutionized various fields, but it’s essential to understand its foundational research to drive continued progress. This study presents a comprehensive overview of classical machine learning algorithms and analyzes the state-of-the-art publications across 12 decades through an extensive bibliometric analysis. The authors analyzed a dataset of highly cited papers from prominent ML conferences and journals, employing citation and keyword analyses to uncover critical insights. The study identifies the most influential papers and authors, reveals evolving collaborative networks within the ML community, and pinpoints prevailing research themes and emerging focus areas. Additionally, it examines the geographic distribution of highly cited publications, highlighting leading countries in ML research. This study provides a valuable overview of traditional learning algorithms’ evolution and their impacts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how machine learning has changed over time. It shows what classic algorithms are important and who has made big contributions to the field. The study also looks at which places are doing most of the research in this area. This is useful information for anyone working with or interested in machine learning. |
Keywords
» Artificial intelligence » Machine learning
