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Integrating Machine Learning with Human Knowledge
Machine learning has been heavily researched and widely used in many disciplines. However, achieving high accuracy requires a large amount of data that is sometimes difficult, expensive, or impractical to obtain. Integrating human knowledge into machine learning can significantly reduce data require...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7588855/ https://www.ncbi.nlm.nih.gov/pubmed/33134890 http://dx.doi.org/10.1016/j.isci.2020.101656 |
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author | Deng, Changyu Ji, Xunbi Rainey, Colton Zhang, Jianyu Lu, Wei |
author_facet | Deng, Changyu Ji, Xunbi Rainey, Colton Zhang, Jianyu Lu, Wei |
author_sort | Deng, Changyu |
collection | PubMed |
description | Machine learning has been heavily researched and widely used in many disciplines. However, achieving high accuracy requires a large amount of data that is sometimes difficult, expensive, or impractical to obtain. Integrating human knowledge into machine learning can significantly reduce data requirement, increase reliability and robustness of machine learning, and build explainable machine learning systems. This allows leveraging the vast amount of human knowledge and capability of machine learning to achieve functions and performance not available before and will facilitate the interaction between human beings and machine learning systems, making machine learning decisions understandable to humans. This paper gives an overview of the knowledge and its representations that can be integrated into machine learning and the methodology. We cover the fundamentals, current status, and recent progress of the methods, with a focus on popular and new topics. The perspectives on future directions are also discussed. |
format | Online Article Text |
id | pubmed-7588855 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-75888552020-10-30 Integrating Machine Learning with Human Knowledge Deng, Changyu Ji, Xunbi Rainey, Colton Zhang, Jianyu Lu, Wei iScience Review Machine learning has been heavily researched and widely used in many disciplines. However, achieving high accuracy requires a large amount of data that is sometimes difficult, expensive, or impractical to obtain. Integrating human knowledge into machine learning can significantly reduce data requirement, increase reliability and robustness of machine learning, and build explainable machine learning systems. This allows leveraging the vast amount of human knowledge and capability of machine learning to achieve functions and performance not available before and will facilitate the interaction between human beings and machine learning systems, making machine learning decisions understandable to humans. This paper gives an overview of the knowledge and its representations that can be integrated into machine learning and the methodology. We cover the fundamentals, current status, and recent progress of the methods, with a focus on popular and new topics. The perspectives on future directions are also discussed. Elsevier 2020-10-09 /pmc/articles/PMC7588855/ /pubmed/33134890 http://dx.doi.org/10.1016/j.isci.2020.101656 Text en © 2020 The Author(s) http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Deng, Changyu Ji, Xunbi Rainey, Colton Zhang, Jianyu Lu, Wei Integrating Machine Learning with Human Knowledge |
title | Integrating Machine Learning with Human Knowledge |
title_full | Integrating Machine Learning with Human Knowledge |
title_fullStr | Integrating Machine Learning with Human Knowledge |
title_full_unstemmed | Integrating Machine Learning with Human Knowledge |
title_short | Integrating Machine Learning with Human Knowledge |
title_sort | integrating machine learning with human knowledge |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7588855/ https://www.ncbi.nlm.nih.gov/pubmed/33134890 http://dx.doi.org/10.1016/j.isci.2020.101656 |
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