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The role of lifelong machine learning in bridging the gap between human and machine learning: A scientometric analysis
WIREs Data Mining and Knowledge Discovery ( IF 6.4 ) Pub Date : 2024-01-10 , DOI: 10.1002/widm.1526 Muhammad Abulaish 1 , Nesar Ahmad Wasi 1 , Shachi Sharma 1
WIREs Data Mining and Knowledge Discovery ( IF 6.4 ) Pub Date : 2024-01-10 , DOI: 10.1002/widm.1526 Muhammad Abulaish 1 , Nesar Ahmad Wasi 1 , Shachi Sharma 1
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Due to advancements in data collection, storage, and processing techniques, machine learning has become a thriving and dominant paradigm. However, one of its main shortcomings is that the classical machine learning paradigm acts in isolation without utilizing the knowledge gained through learning from related tasks in the past. To circumvent this, the concept of Lifelong Machine Learning (LML) has been proposed, with the goal of mimicking how humans learn and acquire cognition. Human learning research has revealed that the brain connects previously learned information while learning new information from a single or small number of examples. Similarly, an LML system continually learns by storing and applying acquired information. Starting with an analysis of how the human brain learns, this paper shows that the LML framework shares a functional structure with the brain when it comes to solving new problems using previously learned information. It also provides a description of the LML framework, emphasizing its similarities to human brain learning. It also provides citation graph generation and scientometric analysis algorithms for the LML literatures, including information about the datasets and evaluation metrics that have been used in the empirical evaluation of LML systems. Finally, it presents outstanding issues and possible future research directions in the field of LML.
中文翻译：
终身机器学习在弥合人类和机器学习之间差距方面的作用：科学计量分析
由于数据收集、存储和处理技术的进步，机器学习已成为一种蓬勃发展的主导范式。然而，其主要缺点之一是经典的机器学习范式是孤立运行的，没有利用过去从相关任务中学习所获得的知识。为了解决这个问题，提出了终身机器学习（LML）的概念，其目标是模仿人类学习和获得认知的方式。人类学习研究表明，大脑会连接先前学到的信息，同时从单个或少量示例中学习新信息。同样，LML 系统通过存储和应用获取的信息来不断学习。本文从分析人脑如何学习开始，表明在使用先前学习的信息解决新问题时，LML 框架与大脑共享功能结构。它还提供了 LML 框架的描述，强调其与人脑学习的相似之处。它还为 LML 文献提供引文图生成和科学计量分析算法，包括有关 LML 系统实证评估中使用的数据集和评估指标的信息。最后，提出了 LML 领域的突出问题和未来可能的研究方向。
更新日期：2024-01-10
中文翻译：
终身机器学习在弥合人类和机器学习之间差距方面的作用：科学计量分析
由于数据收集、存储和处理技术的进步，机器学习已成为一种蓬勃发展的主导范式。然而，其主要缺点之一是经典的机器学习范式是孤立运行的，没有利用过去从相关任务中学习所获得的知识。为了解决这个问题，提出了终身机器学习（LML）的概念，其目标是模仿人类学习和获得认知的方式。人类学习研究表明，大脑会连接先前学到的信息，同时从单个或少量示例中学习新信息。同样，LML 系统通过存储和应用获取的信息来不断学习。本文从分析人脑如何学习开始，表明在使用先前学习的信息解决新问题时，LML 框架与大脑共享功能结构。它还提供了 LML 框架的描述，强调其与人脑学习的相似之处。它还为 LML 文献提供引文图生成和科学计量分析算法，包括有关 LML 系统实证评估中使用的数据集和评估指标的信息。最后，提出了 LML 领域的突出问题和未来可能的研究方向。
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