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Landmark-based Ensemble Learning with Random Fourier Features and Gradient Boosting

Abstract : This paper jointly leverages two state-of-the-art learning strategies gradient boosting (GB) and kernel Random Fourier Features (RFF)-to address the problem of kernel learning. Our study builds on a recent result showing that one can learn a distribution over the RFF to produce a new kernel suited for the task at hand. For learning this distribution, we exploit a GB scheme expressed as ensembles of RFF weak learners, each of them being a kernel function designed to fit the residual. Unlike 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 from the training data as a weighted sum of RFF. This strategy allows one to build a classifier based on a small ensemble of learned kernel "landmarks" better suited for the underlying application. We conduct a thorough experimental analysis to highlight the advantages of our method compared to both boosting-based and kernel-learning state-of-the-art methods.
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Contributor : Léo Gautheron Connect in order to contact the contributor
Submitted on : Wednesday, July 15, 2020 - 5:48:05 PM
Last modification on : Saturday, June 25, 2022 - 9:15:02 PM
Long-term archiving on: : Tuesday, December 1, 2020 - 6:44:28 PM


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  • HAL Id : hal-02900044, version 1



Léo Gautheron, Pascal Germain, Amaury Habrard, Guillaume Metzler, Emilie Morvant, et al.. Landmark-based Ensemble Learning with Random Fourier Features and Gradient Boosting. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Sep 2020, Ghent, Belgium. ⟨hal-02900044⟩



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