Leveraging the Potentials of Dedicated Collaborative Interactive Learning: Conceptual Foundations to Overcome Uncertainty by Human-Machine Collaboration

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dc.contributor.author Calma, Adrian
dc.contributor.author Oeste-Reiß, Sarah
dc.contributor.author Sick, Bernhard
dc.contributor.author Leimeister, Jan Marco
dc.date.accessioned 2017-12-28T00:42:56Z
dc.date.available 2017-12-28T00:42:56Z
dc.date.issued 2018-01-03
dc.description.abstract When a learning system learns from data that was previously assigned to categories, we say that the learning system learns in a supervised way. By "supervised", we mean that a higher entity, for example a human, has arranged the data into categories. Fully categorizing the data is cost intensive and time consuming. Moreover, the categories (labels) provided by humans might be subject to uncertainty, as humans are prone to error. This is where dedicate collaborative interactive learning (D-CIL) comes together: The learning system can decide from which data it learns, copes with uncertainty regarding the categories, and does not require a fully labeled dataset. Against this background, we create the foundations of two central challenges in this early development stage of D-CIL: task complexity and uncertainty. We present an approach to "crowdsourcing traffic sign labels with self-assessment" that will support leveraging the potentials of D-CIL.
dc.format.extent 9 pages
dc.identifier.doi 10.24251/HICSS.2018.120
dc.identifier.isbn 978-0-9981331-1-9
dc.identifier.uri http://hdl.handle.net/10125/50007
dc.language.iso eng
dc.relation.ispartof Proceedings of the 51st Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Decision Support for Complex Networks
dc.subject Active Learning, Crowdsourcing, Collaboration Engineering, Dedicated Collaborative Learning, Human-Machine Collaboration
dc.title Leveraging the Potentials of Dedicated Collaborative Interactive Learning: Conceptual Foundations to Overcome Uncertainty by Human-Machine Collaboration
dc.type Conference Paper
dc.type.dcmi Text
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Decision Support for Complex Networks