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A Case for Guided Machine Learning

  • Florian WestphalEmail author
  • Niklas Lavesson
  • Håkan Grahn
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11713)

Abstract

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.

Keywords

Guided machine learning Interactive machine learning Human-in-the-loop Definition 

Notes

Acknowledgements

The authors would like to thank Huynh Khanh Vi Tran for valuable discussions about possible gML definitions, as well as the anonymous reviewers for their useful comments.

This work is part of the research project “Scalable resource-efficient systems for big data analytics” funded by the Knowledge Foundation (grant: 20140032) in Sweden.

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Copyright information

© IFIP International Federation for Information Processing 2019

Authors and Affiliations

  1. 1.Blekinge Institute of TechnologyKarlskronaSweden
  2. 2.Jönköping UniversityJönköpingSweden

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