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End-User Feature Labeling: Supervised and Semi-supervised Approaches Based on Locally-Weighted Logistic Regression

Creator:

Das, Shubhomoy, Moore, Travis, Wong, Weng-Keen, Stumpf, Simone, Oberst, Ian, McIntosh, Kevin, and Burnetta, Margaret

Abstract:

When intelligent interfaces, such as intelligent desktop assistants, email classifiers, and recommender systems, customize themselves to a particular end user, such customizations can decrease productivity and increase frustration due to inaccurate predictions—especially in early stages when training data is limited. The end user can improve the learning algorithm by tediously...

Resource Type:

Article

Full Text:

† , Margaret Burnett † † Oregon State University, OR, USA ‡ City University London, UK