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Erschienen in:
My Brain Is Out of the Loop: A Neuroergonomic Approach of OOTL Phenomenon Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

CHISSL: A Human-Machine Collaboration Space for Unsupervised Learning

verfasst von : Dustin Arendt, Caner Komurlu, Leslie M. Blaha

Erschienen in: Augmented Cognition. Neurocognition and Machine Learning

Verlag: Springer International Publishing

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Abstract

We developed CHISSL, a human-machine interface that utilizes interactive supervision to help the user group unlabeled instances by her own mental model. The user primarily interacts via correction (moving a misplaced instance into its correct group) or confirmation (accepting that an instance is placed in its correct group). Concurrent with the user’s interactions, CHISSL trains a classification model guided by the user’s grouping of the data. It then predicts the group of unlabeled instances and arranges some of these alongside the instances manually organized by the user. We hypothesize that this mode of human and machine collaboration is more effective than Active Learning, wherein the machine decides for itself which instances should be labeled by the user. We found supporting evidence for this hypothesis in a pilot study where we applied CHISSL to organize a collection of handwritten digits.

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Vorheriges Kapitel Facial Expression Recognition from Still Images
Nächstes Kapitel Toward an Open Data Repository and Meta-Analysis of Cognitive Data Using fNIRS Studies of Emotion
Fußnoten
1
CHISSL stems from a concatenation of the acronyms for computer-human interaction (CHI) and semi-supervised learning (SSL).
 
2
Data available from the UCI Machine Learning Repository [19], as “Optical Recognition of Handwritten Digits Data Set”.
 
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Metadaten
Titel
CHISSL: A Human-Machine Collaboration Space for Unsupervised Learning
verfasst von
Dustin Arendt
Caner Komurlu
Leslie M. Blaha
Copyright-Jahr
2017
Buch
Augmented Cognition. Neurocognition and Machine Learning

Print ISBN: 978-3-319-58627-4

Electronic ISBN: 978-3-319-58628-1

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-58628-1_33

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