Using domain knowledge to improve machine learning
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article number
03654_2022_08_04
A survey of recent advances
Machine learning methods have achieved some impressive results over the past decade. However, this was in large part the result of utilizing large amounts of data and growing computational resources efficiently. To extend this recent success to domains where large quantities of high-quality data are not readily available, the field of informed machine learning has emerged, which aims at integrating preexisting knowledge into machine learning models. The aim of this paper is to provide an overview of the major new developments in this field and to discuss important open problems.
| Authors | Tim Rensmeyer, Samim Multaheb, Julian Putzke, Bernd Zimmering, Oliver Niggemann, |
|---|---|
| Publishing Date | 10 Aug 2022 |
| Format | |
| Publisher | Vulkan-Verlag GmbH |
| Language | German |
| Pages | 9 |
| Title | Using domain knowledge to improve machine learning |
| Subtitle | A survey of recent advances |
| Description | Machine learning methods have achieved some impressive results over the past decade. However, this was in large part the result of utilizing large amounts of data and growing computational resources efficiently. To extend this recent success to domains where large quantities of high-quality data are not readily available, the field of informed machine learning has emerged, which aims at integrating preexisting knowledge into machine learning models. The aim of this paper is to provide an overview of the major new developments in this field and to discuss important open problems. |
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