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Ranking by calibrated AdaBoost
Abstract : This paper describes the ideas and methodologies that we used in the Yahoo learning-to- rank challenge1. Our technique is essentially pointwise with a listwise touch at the last combination step. The main ingredients of our approach are 1) preprocessing (querywise normalization) 2) multi-class AdaBoost.MH 3) regression calibration, and 4) an expo- nentially weighted forecaster for model combination. In post-challenge analysis we found that preprocessing and training AdaBoost with a wide variety of hyperparameters im- proved individual models significantly, the final listwise ensemble step was crucial, whereas calibration helped only in creating diversity.
Cited literature [14 references]
https://hal.inria.fr/hal-00643001
Contributor : Balázs Kégl Connect in order to contact the contributor
Submitted on : Sunday, November 20, 2011 - 10:56:57 PM
Last modification on : Thursday, July 8, 2021 - 3:48:32 AM
Long-term archiving on: : Friday, November 16, 2012 - 11:31:44 AM
Contributor : Balázs Kégl Connect in order to contact the contributor
Submitted on : Sunday, November 20, 2011 - 10:56:57 PM
Last modification on : Thursday, July 8, 2021 - 3:48:32 AM
Long-term archiving on: : Friday, November 16, 2012 - 11:31:44 AM
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