Leveraging the Potentials of Dedicated Collaborative Interactive Learning: Conceptual Foundations to Overcome Uncertainty by Human-Machine Collaboration
ISBN
978-0-9981331-1-9
Type
conference paper
Date Issued
2018
Author(s)
Research Team
IWI6
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.
Language
English
Keywords
Collaborative Interactive Learning
Crowdsourcing
Human-Machine Collaboration
Uncertainty
HSG Classification
contribution to practical use / society
Start page
960
End page
968
Event Title
Hawaii International Conference on System Sciences (HICSS)
Event Location
Waikoloa, HI, USA
Event Date
03.01.2018-06.01.2018
Division(s)
Eprints ID
251712
File(s)![Thumbnail Image]()
open.access
Name
JML_676.pdf
Size
1.08 MB
Format
Adobe PDF
Checksum (MD5)
9dc6b5080077743ea26f8699a634c0c2