3 citations
- Laboratoire Hubert CurienFR9 papers
- Centre National de la Recherche ScientifiqueFR3 papers
- Université Jean MonnetFR3 papers
- Université LavalCA3 papers
- Innate Pharma (France)FR2 papers
- Artificial Intelligence Research InstituteES1 paper
- Centre de Recherche Astrophysique de LyonFR1 paper
- Consejo Superior de Investigaciones CientíficasES1 paper
- École Nationale Supérieure de Mécanique et des MicrotechniquesFR1 paper
- Institut d’Optique Graduate SchoolFR1 paper
- Institute of Science and Technology AustriaAT1 paper
- Institut National Supérieur des Sciences et Techniques d'AbéchéTD1 paper
6 papers · 1 filter
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…
An Improvement to the Domain Adaptation Bound in a PAC-Bayesian context
Pascal Germain, Amaury Habrard, Francois Laviolette +1
This paper provides a theoretical analysis of domain adaptation based on the PAC-Bayesian theory. We propose an improvement of the previous domain adaptation bound obtained by Germ…
On Generalizing the C-Bound to the Multiclass and Multi-label Settings
Francois Laviolette, Emilie Morvant, Liva Ralaivola +1
The C-bound, introduced in Lacasse et al., gives a tight upper bound on the risk of a binary majority vote classifier. In this work, we present a first step towards extending this…
Domain adaptation of weighted majority votes via perturbed variation-based self-labeling
Emilie Morvant
In machine learning, the domain adaptation problem arrives when the test (target) and the train (source) data are generated from different distributions. A key applied issue is thu…
On the Generalization of the C-Bound to Structured Output Ensemble Methods
François Laviolette, Emilie Morvant, Liva Ralaivola +1
This paper generalizes an important result from the PAC-Bayesian literature for binary classification to the case of ensemble methods for structured outputs. We prove a generic ver…
Majority Vote of Diverse Classifiers for Late Fusion
Emilie Morvant, Amaury Habrard, Stéphane Ayache
In the past few years, a lot of attention has been devoted to multimedia indexing by fusing multimodal informations. Two kinds of fusion schemes are generally considered: The early…