Accelerated proximal boosting - Archive ouverte HAL Accéder directement au contenu
Pré-Publication, Document De Travail Année : 2018

Accelerated proximal boosting

Résumé

Gradient boosting is a prediction method that iteratively combines weak learners to produce a complex and accurate model. From an optimization point of view, the learning procedure of gradient boosting mimics a gradient descent on a functional variable. This paper proposes to build upon the proximal point algorithm when the empirical risk to minimize is not differentiable. In addition , the novel boosting approach, called accelerated proximal boosting, benefits from Nesterov's acceleration in the same way as gradient boosting [Biau et al., 2018]. Advantages of leveraging proximal methods for boosting are illustrated by numerical experiments on simulated and real-world data. In particular, we exhibit a favorable comparison over gradient boosting regarding convergence rate and prediction accuracy.
Fichier principal
Vignette du fichier
paper.pdf (2.85 Mo) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01853244 , version 1 (02-08-2018)
hal-01853244 , version 2 (22-01-2020)
hal-01853244 , version 3 (27-07-2021)
hal-01853244 , version 4 (29-11-2022)

Identifiants

  • HAL Id : hal-01853244 , version 1

Citer

Erwan Fouillen, Claire Boyer, Maxime Sangnier. Accelerated proximal boosting. 2018. ⟨hal-01853244v1⟩
379 Consultations
335 Téléchargements

Partager

Gmail Mastodon Facebook X LinkedIn More