增量机器学习:增量分类

Engin Baysal, C. Bayilmis
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引用次数: 0

摘要

增量式机器学习是机器学习领域的研究课题之一。由于有限的内存和处理能力,不断增加的数据大小和种类使得增量学习方法成为必要。在本研究中,通过定义增量机器学习方法的总体框架,确定了增量分类算法的数据焦点变化。此外,随着增量机器学习的理论定义,定义了现有的适合增量机器学习的机器学习算法。文中提到了这些算法的哪些特征适合于增量学习。本研究为研究增量机器学习问题的研究人员提供了一个详细的定义,并定义了增量分类特征,并给出了有关数据中焦点变化的信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Incremental Machine Learning: Incremental Classification
One of the research topics in machine learning is incremental machine learning. The ever-increasing data size and variety in response to the limited memory and processing power make incremental learning approaches mandatory. In this study, focal changes in the data are determined for incremental classification algorithms by defining the general framework of the incremental machine learning approach. In addition, along with the theoretical definition of incremental machine learning, the existing machine learning algorithms that are suitable for incremental machine learning are defined. It is mentioned that in terms of which features of these algorithms are suitable for incremental learning. This study provides a detailed definition for researchers who will work on incremental machine learning issues, as well as defines the incremental classification characteristics and gives information about the focus changes in the data.
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