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  4. Informed Machine Learning - A Taxonomy and Survey of Integrating Knowledge into Learning Systems
 
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29 March 2019
Paper (Preprint, Research Paper, Review Paper, White Paper, etc.)
Titel

Informed Machine Learning - A Taxonomy and Survey of Integrating Knowledge into Learning Systems

Titel Supplements
Published on arXiv
Abstract
Despite its great success, machine learning can have its limits when dealing with insufficient training data. A potential solution is the additional integration of prior knowledge into the training process, which leads to the notion of informed machine learning. In this paper, we present a structured overview of various approaches in this field. First, we provide a definition and propose a concept for informed machine learning, which illustrates its building blocks and distinguishes it from conventional machine learning. Second, we introduce a taxonomy that serves as a classification framework for informed machine learning approaches. It considers the source of knowledge, its representation, and its integration into the machine learning pipeline. Third, we survey related research and describe how different knowledge representations such as algebraic equations, logic rules, or simulation results can be used in learning systems. This evaluation of numerous papers on the basis of our taxonomy uncovers key methods in the field of informed machine learning.
Author(s)
RĂ¼den, Laura von
Fraunhofer-Institut fĂ¼r Intelligente Analyse- und Informationssysteme IAIS
Mayer, Sebastian
Fraunhofer-Institut fĂ¼r Algorithmen und Wissenschaftliches Rechnen SCAI
Beckh, Katharina
Fraunhofer-Institut fĂ¼r Intelligente Analyse- und Informationssysteme IAIS
Georgiev, Bogdan
Fraunhofer-Institut fĂ¼r Intelligente Analyse- und Informationssysteme IAIS
Giesselbach, Sven
Fraunhofer-Institut fĂ¼r Intelligente Analyse- und Informationssysteme IAIS
Heese, Raoul
Fraunhofer-Institut fĂ¼r Techno- und Wirtschaftsmathematik ITWM
Kirsch, Birgit
Fraunhofer-Institut fĂ¼r Intelligente Analyse- und Informationssysteme IAIS
Pfrommer, Julius
Fraunhofer-Institut fĂ¼r Optronik, Systemtechnik und Bildauswertung IOSB
Pick, Annika
Fraunhofer-Institut fĂ¼r Intelligente Analyse- und Informationssysteme IAIS
Ramamurthy, Rajkumar
Fraunhofer-Institut fĂ¼r Intelligente Analyse- und Informationssysteme IAIS
Schuecker, Jannis
Fraunhofer-Institut fĂ¼r Intelligente Analyse- und Informationssysteme IAIS
Garcke, Jochen
Fraunhofer-Institut fĂ¼r Algorithmen und Wissenschaftliches Rechnen SCAI
Bauckhage, Christian
Fraunhofer-Institut fĂ¼r Intelligente Analyse- und Informationssysteme IAIS
Walczak, Michal
Fraunhofer-Institut fĂ¼r Techno- und Wirtschaftsmathematik ITWM
Project(s)
ML2R
Funder
Bundesministerium fĂ¼r Bildung und Forschung -BMBF-
Thumbnail Image
DOI
10.48550/arXiv.1903.12394
Externer Link
Externer Link
Language
English
google-scholar
Fraunhofer-Institut fĂ¼r Algorithmen und Wissenschaftliches Rechnen SCAI
Fraunhofer-Institut fĂ¼r Intelligente Analyse- und Informationssysteme IAIS
Fraunhofer-Institut fĂ¼r Optronik, Systemtechnik und Bildauswertung IOSB
Fraunhofer-Institut fĂ¼r Techno- und Wirtschaftsmathematik ITWM
Tags
  • machine learning

  • prior knowledge

  • expert knowledge

  • Informed

  • hybrid

  • survey

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