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Conference Papers Year : 2011

Ranking by calibrated AdaBoost

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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.
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Dates and versions

hal-00643001 , version 1 (20-11-2011)

Identifiers

  • HAL Id : hal-00643001 , version 1

Cite

Róbert Busa-Fekete, Balázs Kégl, Tamas Elteto, György Szarvas. Ranking by calibrated AdaBoost. Yahoo! Learning to Rank Challenge, Jun 2010, Haifa, Israel. ⟨hal-00643001⟩
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