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A Case for Guided Machine Learning
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.ORCID iD: 0000-0002-2161-7371
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.ORCID iD: 0000-0001-9947-1088
2019 (English)In: Machine Learning and Knowledge Extraction / [ed] Andreas Hozinger, Peter Kieseberg, A Min Tjoa and Edgar Weippl, Springer, 2019, p. 353-361Conference paper, Published paper (Refereed)
Abstract [en]

Involving humans in the learning process of a machine learning algorithm can have many advantages ranging from establishing trust into a particular model to added personalization capabilities to reducing labeling efforts. While these approaches are commonly summarized under the term interactive machine learning (iML), no unambiguous definition of iML exists to clearly define this area of research. In this position paper, we discuss the shortcomings of current definitions of iML and propose and define the term guided machine learning (gML) as an alternative.

Place, publisher, year, edition, pages
Springer, 2019. p. 353-361
Keywords [en]
guided machine learning, interactive machine learning, human-in-the-loop, definition
National Category
Human Computer Interaction Computer Sciences
Identifiers
URN: urn:nbn:se:bth-18708DOI: 10.1007/978-3-030-29726-8_22ISBN: 978-3-030-29726-8 (electronic)OAI: oai:DiVA.org:bth-18708DiVA, id: diva2:1355347
Conference
International Cross-Domain Conference for Machine Learning and Knowledge Extraction, Canterbury 26-29 August
Projects
Scalable resource-efficient systems for big data analytics
Funder
Knowledge Foundation, 20140032Available from: 2019-09-27 Created: 2019-09-27 Last updated: 2019-10-09Bibliographically approved

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a_case_for_guided_machine_learning.pdf(247 kB)18 downloads
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Publisher's full texthttps://doi.org/10.1007

Authority records BETA

Westphal, FlorianLavesson, NiklasGrahn, Håkan

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