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User Feedback and Uncertainty in User Guided Binarization
Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.ORCID iD: 0000-0002-2161-7371
Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.ORCID iD: 0000-0001-9947-1088
Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL). Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.ORCID iD: 0000-0002-0535-1761
2018 (English)In: International Conference on Data Mining Workshops / [ed] H. Tong, Z. Li, F. Zhu, & J. Yu, IEEE Computer Society, 2018, p. 403-410, article id 8637367Conference paper, Published paper (Refereed)
Abstract [en]

In a child’s development, the child’s inherent ability to construct knowledge from new information is as important as explicit instructional guidance. Similarly, mechanisms to produce suitable learning representations, which can be trans- ferred and allow integration of new information are important for artificial learning systems. However, equally important are modes of instructional guidance, which allow the system to learn efficiently. Thus, the challenge for efficient learning is to identify suitable guidance strategies together with suitable learning mechanisms.

In this paper, we propose guided machine learning as source for suitable guidance strategies, we distinguish be- tween sample selection based and privileged information based strategies and evaluate three sample selection based strategies on a simple transfer learning task. The evaluated strategies are random sample selection, i.e., supervised learning, user based sample selection based on readability, and user based sample selection based on readability and uncertainty. We show that sampling based on readability and uncertainty tends to produce better learning results than the other two strategies. Furthermore, we evaluate the use of the learner’s uncertainty for self directed learning and find that effects similar to the Dunning-Kruger effect prevent this use case. The learning task in this study is document image binarization, i.e., the separation of text foreground from page background and the source domain of the transfer are texts written on paper in Latin characters, while the target domain are texts written on palm leaves in Balinese script.

Place, publisher, year, edition, pages
IEEE Computer Society, 2018. p. 403-410, article id 8637367
Keywords [en]
guided machine learning, interactive machine learning, image binarization, historical documents
National Category
Computer Vision and Robotics (Autonomous Systems) Human Computer Interaction
Identifiers
URN: urn:nbn:se:hj:diva-45615DOI: 10.1109/ICDMW.2018.00066ISI: 000465766800058ISBN: 978-1-5386-9288-2 (electronic)OAI: oai:DiVA.org:hj-45615DiVA, id: diva2:1343929
Conference
18th IEEE International Conference on Data Mining Workshops, ICDMW, Singapore; Singapore; 17 November 2018 through 20 November
Funder
Knowledge Foundation, 20140032Available from: 2019-08-19 Created: 2019-08-19 Last updated: 2019-08-19Bibliographically approved

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Westphal, FlorianGrahn, HåkanLavesson, Niklas
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