End-User Feature Labeling: A Locally-Weighted Regression Approach

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 labeling a substantial amount of additional training data, but this takes time and is too ad hoc to target a particular area of inaccuracy.

Provided by: Association for Computing Machinery Topic: Software Date Added: Feb 2011 Format: PDF

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