15 citations · 47 across the 13 of their papers we have counts for
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stat.ML2019
Learning Landmark-Based Ensembles with Random Fourier Features and Gradient Boosting
Léo Gautheron, Pascal Germain, Amaury Habrard +3
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…
stat.ML2017★ 1 cited
L-SVMs: Landmarks-based Linear Local Support Vectors Machines
Valentina Zantedeschi, Rémi Emonet, Marc Sebban
For their ability to capture non-linearities in the data and to scale to large training sets, local Support Vector Machines (SVMs) have received a special attention during the past…