%0 Conference Proceedings
%T A Case for Guided Machine Learning
%+ Blekinge Institute of Technology [Karlskrona] (BTH)
%+ Jönköping University [Sweden]
%A Westphal, Florian
%A Lavesson, Niklas
%A Grahn, Håkan
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 3rd International Cross-Domain Conference for Machine Learning and Knowledge Extraction (CD-MAKE)
%C Canterbury, United Kingdom
%Y Andreas Holzinger
%Y Peter Kieseberg
%Y A Min Tjoa
%Y Edgar Weippl
%I Springer International Publishing
%3 Machine Learning and Knowledge Extraction
%V LNCS-11713
%P 353-361
%8 2019-08-26
%D 2019
%R 10.1007/978-3-030-29726-8_22
%K Guided machine learning
%K Interactive machine learning
%K Human-in-the-loop
%K Definition
%Z Computer Science [cs]Conference papers
%X 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.
%G English
%Z TC 5
%Z TC 12
%Z WG 8.4
%Z WG 8.9
%Z WG 12.9
%2 https://inria.hal.science/hal-02520045/document
%2 https://inria.hal.science/hal-02520045/file/485369_1_En_22_Chapter.pdf
%L hal-02520045
%U https://inria.hal.science/hal-02520045
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC
%~ IFIP-TC5
%~ IFIP-WG
%~ IFIP-TC12
%~ IFIP-WG8-4
%~ IFIP-WG8-9
%~ IFIP-CD-MAKE
%~ IFIP-WG12-9
%~ IFIP-LNCS-11713