Real-time interaction with supervised learning

Authors:
Rebecca Fiebrink
Affiliations:
Princeton University, Princeton, NJ, USA
Venue:
CHI '10 Extended Abstracts on Human Factors in Computing Systems
Year:
2010

Citing 4
Cited 2

Interactive machine learning

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Abstract

My work concerns the design of interfaces for effective interaction with machine learning algorithms in real-time application domains. I am interested in supporting human interaction throughout the entire supervised learning process, including the generation of training examples. In my dissertation research, I seek to better understand how new machine learning interfaces might improve accessibility and usefulness to non-technical users, to further explore how differences between machine learning in practice and machine learning in theory can inform both interface and algorithm design, and to employ new machine learning interfaces for novel applications in real-time music composition and performance.