FastForest: Learning Gradient-Boosted Regression Trees for Classiﬁcation, Regression and Ranking
“FastForest: Learning Gradient-Boosted Regression Trees for Classiﬁcation, Regression and Ranking” is supported by Ca’ Foscari University of Venice Starting Grant (Fondo Primo Insediamento).
Abstract. Gradient Boosted Regression Trees (GBRTs)are today considered one of the most eﬀective machine learning tools. Indeed, they are exploited within a Learning-to-Rank (LtR) framework by major Web companies including Microsoft, Google, Yahoo, Amazon, Facebook, etc., and learning high-quality GBRT models is remarkably expensive. The goal of our proposal is to design a novel learning algorithms for the learning more eﬃcient and eﬀective GBRT models.
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