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A case for guided machine learning
Blekinge Institute of Technology, Karlskrona, Sweden.ORCID iD: 0000-0002-2161-7371
Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL). Blekinge Institute of Technology, Karlskrona, Sweden.ORCID iD: 0000-0002-0535-1761
Blekinge Institute of Technology, Karlskrona, Sweden.ORCID iD: 0000-0001-9947-1088
2019 (English)In: Machine learning and knowledge extraction: Third IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2019, Canterbury, UK, August 26–29, 2019, Proceedings / [ed] A. Holzinger, P. Kieseberg, A. M. Tjoa & E. Weippl, Cham: 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
Cham: Springer, 2019. p. 353-361
Series
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN 0302-9743 ; 11713
Keywords [en]
Definition, Guided machine learning, Human-in-the-loop, Interactive machine learning, Data mining, Extraction, Learning algorithms, Current definition, Learning process, Personalizations, Position papers, Machine learning
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:hj:diva-46552DOI: 10.1007/978-3-030-29726-8_22Scopus ID: 2-s2.0-85072874206ISBN: 978-3-030-29725-1 (print)ISBN: 978-3-030-29726-8 (electronic)OAI: oai:DiVA.org:hj-46552DiVA, id: diva2:1360792
Conference
International IFIP Cross Domain (CD) Conference for Machine Learning & Knowledge Extraction (MAKE) CD-MAKE 2019, Canterbury, UK, August 26–29, 2019
Funder
Knowledge Foundation, 20140032Available from: 2019-10-14 Created: 2019-10-14 Last updated: 2019-10-14Bibliographically approved

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Lavesson, Niklas

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