计算机科学
学习迁移
范围(计算机科学)
域适应
样品(材料)
领域(数学分析)
人工智能
光学(聚焦)
机器学习
适应(眼睛)
情境伦理学
选择(遗传算法)
数学
数学分析
化学
物理
色谱法
分类器(UML)
法学
政治学
光学
程序设计语言
作者
Asmaul Hosna,Ethel Merry,Jigmey Gyalmo,Zulfikar Alom,Zeyar Aung,Mohammad Abdul Azim
标识
DOI:10.1186/s40537-022-00652-w
摘要
Infinite numbers of real-world applications use Machine Learning (ML) techniques to develop potentially the best data available for the users. Transfer learning (TL), one of the categories under ML, has received much attention from the research communities in the past few years. Traditional ML algorithms perform under the assumption that a model uses limited data distribution to train and test samples. These conventional methods predict target tasks undemanding and are applied to small data distribution. However, this issue conceivably is resolved using TL. TL is acknowledged for its connectivity among the additional testing and training samples resulting in faster output with efficient results. This paper contributes to the domain and scope of TL, citing situational use based on their periods and a few of its applications. The paper provides an in-depth focus on the techniques; Inductive TL, Transductive TL, Unsupervised TL, which consists of sample selection, and domain adaptation, followed by contributions and future directions.
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