Classical Machine Learning: Seventy Years of Algorithmic Learning Evolution

• URL: http://arxiv.org/abs/2408.01747v2
• Date: Mon, 19 Aug 2024 17:31:07 GMT
• Title: Classical Machine Learning: Seventy Years of Algorithmic Learning Evolution
• Authors: Absalom E. Ezugwu, Yuh-Shan Ho, Ojonukpe S. Egwuche, Olufisayo S. Ekundayo, Annette Van Der Merwe, Apu K. Saha, Jayanta Pal,
• Abstract summary: Machine learning (ML) has transformed numerous fields, but understanding its foundational research is crucial for its continued progress. This paper presents an overview of the significant classical ML algorithms and examines the state-of-the-art publications spanning twelve decades. We analyzed a dataset of highly cited papers from prominent ML conferences and journals, employing citation and keyword analyses to uncover critical insights.
• Score: 1.121816400852218
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Machine learning (ML) has transformed numerous fields, but understanding its foundational research is crucial for its continued progress. This paper presents an overview of the significant classical ML algorithms and examines the state-of-the-art publications spanning twelve decades through an extensive bibliometric analysis study. We analyzed a dataset of highly cited papers from prominent ML conferences and journals, employing citation and keyword analyses to uncover critical 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 focus areas. 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. It discusses challenges and opportunities for future development, focusing 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.

Related papers

• Enhancing literature review with LLM and NLP methods. Algorithmic trading case [0.0]
  This study utilizes machine learning algorithms to analyze and organize knowledge in the field of algorithmic trading. By filtering a dataset of 136 million research papers, we identified 14,342 relevant articles published between 1956 and Q1 2020.
  arXiv  Detail & Related papers  (2024-10-23T13:37:27Z)
• Learning the Bitter Lesson: Empirical Evidence from 20 Years of CVPR Proceedings [1.3812010983144802]
  This study examines the alignment of emphConference on Computer Vision and Pattern Recognition (CVPR) research with the principles of the "bitter lesson" proposed by Rich Sutton. We analyze two decades of CVPR abstracts and titles using large language models (LLMs) to assess the field's embracement of these principles.
  arXiv  Detail & Related papers  (2024-10-12T21:06:13Z)
• Retrieval-Enhanced Machine Learning: Synthesis and Opportunities [60.34182805429511]
  Retrieval-enhancement can be extended to a broader spectrum of machine learning (ML) This work introduces a formal framework of this paradigm, Retrieval-Enhanced Machine Learning (REML), by synthesizing the literature in various domains in ML with consistent notations which is missing from the current literature. The goal of this work is to equip researchers across various disciplines with a comprehensive, formally structured framework of retrieval-enhanced models, thereby fostering interdisciplinary future research.
  arXiv  Detail & Related papers  (2024-07-17T20:01:21Z)
• Ontology Embedding: A Survey of Methods, Applications and Resources [54.3453925775069]
  Ontologies are widely used for representing domain knowledge and meta data. One straightforward solution is to integrate statistical analysis and machine learning. Numerous papers have been published on embedding, but a lack of systematic reviews hinders researchers from gaining a comprehensive understanding of this field.
  arXiv  Detail & Related papers  (2024-06-16T14:49:19Z)
• Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmark [55.898771405172155]
  Federated learning has emerged as a promising paradigm for privacy-preserving collaboration among different parties. We provide a systematic overview of the important and recent developments of research on federated learning.
  arXiv  Detail & Related papers  (2023-11-12T06:32:30Z)
• A Comprehensive Study of Groundbreaking Machine Learning Research: Analyzing highly cited and impactful publications across six decades [1.6442870218029522]
  Machine learning (ML) has emerged as a prominent field of research in computer science and other related fields. It is crucial to understand the landscape of highly cited publications to identify key trends, influential authors, and significant contributions made thus far.
  arXiv  Detail & Related papers  (2023-08-01T21:43:22Z)
• A Comprehensive Overview of Large Language Models [68.22178313875618]
  Large Language Models (LLMs) have recently demonstrated remarkable capabilities in natural language processing tasks. This article provides an overview of the existing literature on a broad range of LLM-related concepts.
  arXiv  Detail & Related papers  (2023-07-12T20:01:52Z)
• Interpretability of Machine Learning: Recent Advances and Future Prospects [21.68362950922772]
  The proliferation of machine learning (ML) has drawn unprecedented interest in the study of various multimedia contents. The black-box nature of contemporary ML, especially in deep neural networks (DNNs), has posed a primary challenge for ML-based representation learning. This paper presents a survey on recent advances and future prospects on interpretability of ML.
  arXiv  Detail & Related papers  (2023-04-30T17:31:29Z)
• A Survey on Few-Shot Class-Incremental Learning [11.68962265057818]
  Few-shot class-incremental learning (FSCIL) poses a significant challenge for deep neural networks to learn new tasks. This paper provides a comprehensive survey on FSCIL. FSCIL has achieved impressive achievements in various fields of computer vision.
  arXiv  Detail & Related papers  (2023-04-17T10:15:08Z)
• Investigating Fairness Disparities in Peer Review: A Language Model Enhanced Approach [77.61131357420201]
  We conduct a thorough and rigorous study on fairness disparities in peer review with the help of large language models (LMs) We collect, assemble, and maintain a comprehensive relational database for the International Conference on Learning Representations (ICLR) conference from 2017 to date. We postulate and study fairness disparities on multiple protective attributes of interest, including author gender, geography, author, and institutional prestige.
  arXiv  Detail & Related papers  (2022-11-07T16:19:42Z)
• Deep Learning to See: Towards New Foundations of Computer Vision [88.69805848302266]
  This book criticizes the supposed scientific progress in the field of computer vision. It proposes the investigation of vision within the framework of information-based laws of nature.
  arXiv  Detail & Related papers  (2022-06-30T15:20:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.