|NCRN Virtual Seminar: Boosting Models for Edit, Imputation and Prediction of Multiple Response Outcomes||
Abstract: In this paper, we propose a statistical framework that generalizes the classical logit model to predict multiple responses (i.e., multi-label classification). We develop an effective implementation based on boosting and trees. For the NCRN seminar we present an application to editing and imputation in the multiple response race and ethnicity coding on the American Community Survey.
-Cornell University, Ithaca campus: Ives 381
For more locations, see http://www.ncrn.info/event/ncrn-virtual-seminar-feb-5-2014
A recording of the seminar (streaming video) will be available from this link about 5 minutes after the start of the seminar, and will be accessible on-demand afterwards
|Location NCRN Videoconferencing, Ives 381|
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