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A Case for Guided Machine Learning
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.
Cited literature [20 references]
https://hal.inria.fr/hal-02520045
Contributor : Hal Ifip <>
Submitted on : Thursday, March 26, 2020 - 1:49:39 PM
Last modification on : Tuesday, March 31, 2020 - 3:50:37 PM
Long-term archiving on: : Saturday, June 27, 2020 - 2:41:35 PM
Contributor : Hal Ifip <>
Submitted on : Thursday, March 26, 2020 - 1:49:39 PM
Last modification on : Tuesday, March 31, 2020 - 3:50:37 PM
Long-term archiving on: : Saturday, June 27, 2020 - 2:41:35 PM
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Distributed under a Creative Commons Attribution 4.0 International License