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

Simple item page
dc.contributor.authorCalma, Adrian
dc.contributor.authorOeste-Reiß, Sarah
dc.contributor.authorSick, Bernhard
dc.contributor.authorLeimeister, Jan Marco
dc.date.accessioned2017-12-28T00:42:56Z
dc.date.available2017-12-28T00:42:56Z
dc.date.issued2018-01-03
dc.description.abstractWhen 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.extent9 pages
dc.identifier.doi10.24251/HICSS.2018.120
dc.identifier.isbn978-0-9981331-1-9
dc.identifier.urihttp://hdl.handle.net/10125/50007
dc.language.isoeng
dc.relation.ispartofProceedings of the 51st Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectDecision Support for Complex Networks
dc.subjectActive Learning, Crowdsourcing, Collaboration Engineering, Dedicated Collaborative Learning, Human-Machine Collaboration
dc.titleLeveraging the Potentials of Dedicated Collaborative Interactive Learning: Conceptual Foundations to Overcome Uncertainty by Human-Machine Collaboration
dc.typeConference Paper
dc.type.dcmiText

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
paper0120.pdf
Size:
1.11 MB
Format:
Adobe Portable Document Format
Download

Collections

Decision Support for Complex Networks