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

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 structure...

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Vydané v:	IEEE transactions on knowledge and data engineering Ročník 35; číslo 1; s. 614 - 633
Hlavní autori:	von Rueden, Laura, Mayer, Sebastian, Beckh, Katharina, Georgiev, Bogdan, Giesselbach, Sven, Heese, Raoul, Kirsch, Birgit, Pfrommer, Julius, Pick, Annika, Ramamurthy, Rajkumar, Walczak, Michal, Garcke, Jochen, Bauckhage, Christian, Schuecker, Jannis
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.01.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	expert knowledge hybrid informed Knowledge representation Machine learning Mathematical model neuro-symbolic Pipelines prior knowledge survey Systematics Taxonomy Training Training data
ISSN:	1041-4347, 1558-2191
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