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Learning Landmark-Based Ensembles with Random Fourier Features and Gradient Boosting

Léo Gautheron 1 Pascal Germain 2 Amaury Habrard 1 Emilie Morvant 1 Marc Sebban 1 Valentina Zantedeschi 1
2 MODAL - MOdel for Data Analysis and Learning
LPP - Laboratoire Paul Painlevé - UMR 8524, Université de Lille, Sciences et Technologies, Inria Lille - Nord Europe, METRICS - Evaluation des technologies de santé et des pratiques médicales - ULR 2694, Polytech Lille - École polytechnique universitaire de Lille
Abstract : We propose a Gradient Boosting algorithm for learning an ensemble of kernel functions adapted to the task at hand. Unlike state-of-the-art Multiple Kernel Learning techniques that make use of a pre-computed dictionary of kernel functions to select from, at each iteration we fit a kernel by approximating it as a weighted sum of Random Fourier Features (RFF) and by optimizing their barycenter. This allows us to obtain a more versatile method, easier to setup and likely to have better performance. Our study builds on a recent result showing one can learn a kernel from RFF by computing the minimum of a PAC-Bayesian bound on the kernel alignment generalization loss, which is obtained efficiently from a closed-form solution. We conduct an experimental analysis to highlight the advantages of our method w.r.t. both Boosting-based and kernel-learning state-of-the-art methods.
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Preprints, Working Papers, ...
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Contributor : Léo Gautheron Connect in order to contact the contributor
Submitted on : Friday, June 14, 2019 - 1:57:54 PM
Last modification on : Friday, November 27, 2020 - 2:18:03 PM


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  • HAL Id : hal-02148618, version 1
  • ARXIV : 1906.06203


Léo Gautheron, Pascal Germain, Amaury Habrard, Emilie Morvant, Marc Sebban, et al.. Learning Landmark-Based Ensembles with Random Fourier Features and Gradient Boosting. 2019. ⟨hal-02148618⟩



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