Show simple item record

dc.contributor.advisorFogarty, James !en_US
dc.contributor.authorAmershi, Saleemaen_US
dc.date.accessioned2013-02-25T18:01:21Z
dc.date.available2013-02-25T18:01:21Z
dc.date.issued2013-02-25
dc.date.submitted2012en_US
dc.identifier.otherAmershi_washington_0250E_11033.pdfen_US
dc.identifier.urihttp://hdl.handle.net/1773/22006
dc.descriptionThesis (Ph.D.)--University of Washington, 2012en_US
dc.description.abstractEnd-user interactive machine learning is a promising tool for enhancing human capabilities with data. Recent work has shown that we can create specific applications that employ end-user interactive machine learning. However, we still lack a generalized understanding of how to design effective end-user interaction with machine learning. This dissertation advances our understanding of this problem by demonstrating effective end-user interaction with machine learning in a variety of new situations and by characterizing the design factors affecting the end-user interactive machine learning process itself. Specifically, this dissertation presents (1) new interaction techniques for end-user creation of image classifiers in an existing end-user interactive machine learning system called CueFlik, (2) a novel system called ReGroup that employs end-user interactive machine learning for the purpose of access control in social networks, (3) a novel system called CueT that supports end-user driven machine learning for computer network alarm triage, and (4) a novel design space characterizing the goals and constraints impacting the end-user interactive machine learning process itself. Together, these contributions can move us beyond ad-hoc designs for specific applications and provide a foundation for future researchers and developers of end-user interactive machine learning systems.en_US
dc.format.mimetypeapplication/pdfen_US
dc.language.isoen_USen_US
dc.rightsCopyright is held by the individual authors.en_US
dc.subjectHuman-computer interaction; Machine learningen_US
dc.subject.otherComputer scienceen_US
dc.subject.otherComputer science and engineeringen_US
dc.titleDesigning for Effective End-User Interaction with Machine Learningen_US
dc.typeThesisen_US
dc.embargo.termsNo embargoen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record