End-user feature labeling: Supervised and semi-supervised approaches based on locally-weighted logistic regression

  title={End-user feature labeling: Supervised and semi-supervised approaches based on locally-weighted logistic regression},
  author={Shubhomoy Das and Travis Moore and Weng-Keen Wong and Simone Stumpf and Ian Oberst and Kevin McIntosh and Margaret M. Burnett},
  journal={Artif. Intell.},
When intelligent interfaces, such as intelligent desktop assistants, email classif iers, and recommender systems, customize themselves to a particular end user , such customizations can decrease productivity and increase frustration due to inacc urate predictions—especially in early stages when training data is limited. The end user can improve the learning algorithm by tediously labeling a substantial amoun t of additional training data, but this takes time and is too ad hoc to target a… CONTINUE READING


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