Taxonomy of machine learning paradigms: A data-centric perspective. Emmert-Streib, F. & Dehmer, M. WIREs Data Mining and Knowledge Discovery, 12(5):e1470, 2022. _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/widm.1470![Taxonomy of machine learning paradigms: A data-centric perspective [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Machine learning is a field composed of various pillars. Traditionally, supervised learning (SL), unsupervised learning (UL), and reinforcement learning (RL) are the dominating learning paradigms that inspired the field since the 1950s. Based on these, thousands of different methods have been developed during the last seven decades used in nearly all application domains. However, recently, other learning paradigms are gaining momentum which complement and extend the above learning paradigms significantly. These are multi-label learning (MLL), semi-supervised learning (SSL), one-class classification (OCC), positive-unlabeled learning (PUL), transfer learning (TL), multi-task learning (MTL), and one-shot learning (OSL). The purpose of this article is a systematic discussion of these modern learning paradigms and their connection to the traditional ones. We discuss each of the learning paradigms formally by defining key constituents and paying particular attention to the data requirements for allowing an easy connection to applications. That means, we assume a data-driven perspective. This perspective will also allow a systematic identification of relations between the individual learning paradigms in the form of a learning-paradigm graph (LP-graph). Overall, the LP-graph establishes a taxonomy among 10 different learning paradigms. This article is categorized under: Technologies \textgreater Machine Learning Application Areas \textgreater Science and Technology Fundamental Concepts of Data and Knowledge \textgreater Key Design Issues in Data Mining
@article{emmert-streib_taxonomy_2022,
title = {Taxonomy of machine learning paradigms: {A} data-centric perspective},
volume = {12},
issn = {1942-4795},
shorttitle = {Taxonomy of machine learning paradigms},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/widm.1470},
doi = {10.1002/widm.1470},
abstract = {Machine learning is a field composed of various pillars. Traditionally, supervised learning (SL), unsupervised learning (UL), and reinforcement learning (RL) are the dominating learning paradigms that inspired the field since the 1950s. Based on these, thousands of different methods have been developed during the last seven decades used in nearly all application domains. However, recently, other learning paradigms are gaining momentum which complement and extend the above learning paradigms significantly. These are multi-label learning (MLL), semi-supervised learning (SSL), one-class classification (OCC), positive-unlabeled learning (PUL), transfer learning (TL), multi-task learning (MTL), and one-shot learning (OSL). The purpose of this article is a systematic discussion of these modern learning paradigms and their connection to the traditional ones. We discuss each of the learning paradigms formally by defining key constituents and paying particular attention to the data requirements for allowing an easy connection to applications. That means, we assume a data-driven perspective. This perspective will also allow a systematic identification of relations between the individual learning paradigms in the form of a learning-paradigm graph (LP-graph). Overall, the LP-graph establishes a taxonomy among 10 different learning paradigms. This article is categorized under: Technologies {\textgreater} Machine Learning Application Areas {\textgreater} Science and Technology Fundamental Concepts of Data and Knowledge {\textgreater} Key Design Issues in Data Mining},
language = {en},
number = {5},
urldate = {2023-03-08},
journal = {WIREs Data Mining and Knowledge Discovery},
author = {Emmert-Streib, Frank and Dehmer, Matthias},
year = {2022},
note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/widm.1470},
keywords = {artificial intelligence, machine learning, multi-label learning, multi-task learning, transfer learning},
pages = {e1470},
}
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