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Cited by 561

The DOI matches its Crossref record; 48 of the 48 references it cites were found in a registry or an index.

Transfer learning: a friendly introduction

Open access
Hosna, Asmaul, Merry, Ethel, Gyalmo, Jigmey, Alom, Zulfikar, Aung, Zeyar, Azim, Mohammad Abdul·2022·Journal of Big Data

Abstract

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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Metadata

Authors
Hosna, A., Merry, E., Gyalmo, J., Alom, Z., Aung, Z., & Azim, M. A.
Venue
Journal of Big Data
Published
2022-10-22
Volume
9
Issue
1
Pages
102-102
ISSN
2196-1115 (ISSN-L)
OpenAlex
W4307135222
Cited by
561
References
48
FWCI
33.85
Type
journal article
Language
en
Open access
gold

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CoChat. (2026). Citation check: Transfer learning: a friendly introduction. https://app.cochat.ai/papers/10.1186/s40537-022-00652-w
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Hosna, A., Merry, E., Gyalmo, J., Alom, Z., Aung, Z., & Azim, M. A. (2022). Transfer learning: a friendly introduction. Journal of Big Data. https://doi.org/10.1186/s40537-022-00652-w

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.