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A Case for Guided Machine Learning
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.ORCID iD: 0000-0002-2161-7371
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.ORCID iD: 0000-0001-9947-1088
2019 (English)In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) / [ed] Andreas Hozinger, Peter Kieseberg, A Min Tjoa and Edgar Weippl, Springer, 2019, Vol. 11713, 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
Springer, 2019. Vol. 11713, p. 353-361
Keywords [en]
guided machine learning, interactive machine learning, human-in-the-loop, definition
National Category
Human Computer Interaction Computer Sciences
Identifiers
URN: urn:nbn:se:bth-18708DOI: 10.1007/978-3-030-29726-8_22ISBN: 978-3-030-29726-8 (electronic)OAI: oai:DiVA.org:bth-18708DiVA, id: diva2:1355347
Conference
3rd IFIP Cross Domain Conference for Machine Learning and Knowledge Extraction, CD-MAKE 2019; Canterbury; United Kingdom; 26 August 2019 through 29 August
Projects
Scalable resource-efficient systems for big data analytics
Funder
Knowledge Foundation, 20140032Available from: 2019-09-27 Created: 2019-09-27 Last updated: 2020-05-29Bibliographically approved
In thesis
1. Data and Time Efficient Historical Document Analysis
Open this publication in new window or tab >>Data and Time Efficient Historical Document Analysis
2020 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Over the last decades companies and government institutions have gathered vast collections of images of historical handwritten documents. In order to make these collections truly useful to the broader public, images suffering from degradations, such as faded ink, bleed through or stains, need to be made readable and the collections as a whole need to be made searchable. Readability can be achieved by separating text foreground from page background using document image binarization, while searchability by search string or by example image can be achieved through word spotting. Developing algorithms with reasonable binarization or word spotting performance is a difficult task. Additional challenges are to make these algorithms execute fast enough to process vast collections of images in a reasonable amount of time, and to enable them to learn from few labeled training samples. In this thesis, we explore heterogeneous computing, parameter prediction, and enhanced throughput as ways to reduce the execution time of document image binarization algorithms. We find that parameter prediction and mapping a heuristics based binarization algorithm to the GPU lead to an 1.7 and 3.5 increase in execution performance respectively. Furthermore, we identify for a learning based binarization algorithm using recurrent neural networks the number of pixels processed at once as way to trade off execution time with binarization quality. The achieved increase in throughput results in a 3.8 times faster overall execution time. Additionally, we explore guided machine learning (gML) as a possible approach to reduce the required amount of training data for learning based algorithms for binarization, character recognition and word spotting. We propose an initial gML system for binarization, which allows a user to improve an algorithm’s binarization quality by selecting suitable training samples. Based on this system, we identify and pursue three different directions, viz., formulation of a clear definition of gML, identification of an efficient knowledge transfer mechanism from user to learner, and automation of sample selection. We explore the Learning Using Privileged Information paradigm as a possible knowledge transfer mechanism by using character graphs as privileged information for training a neural network based character recognizer. Furthermore, we show that, given a suitable word image representation, automatic sample selection can help to reduce the amount of training data required for word spotting by up to 69%.

Place, publisher, year, edition, pages
Karlskrona: Blekinge Tekniska Högskola, 2020. p. 202
Series
Blekinge Institute of Technology Doctoral Dissertation Series, ISSN 1653-2090 ; 5
National Category
Computer Engineering Computer Sciences Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:bth-19529 (URN)978-91-7295-404-5 (ISBN)
Public defence
2020-09-03, J1630, Valhallavägen 1, Karlskrona, 13:15 (English)
Opponent
Supervisors
Funder
Knowledge Foundation, 20140032
Available from: 2020-05-29 Created: 2020-05-29 Last updated: 2020-05-29Bibliographically approved

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Publisher's full texthttps://doi.org/10.1007

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Westphal, FlorianLavesson, NiklasGrahn, Håkan

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