30 citations · 125 across the 26 of their papers we have counts for
3 papers · 1 filter
PAC-Bayesian Learning and Domain Adaptation
Pascal Germain, Amaury Habrard, François Laviolette +1
In machine learning, Domain Adaptation (DA) arises when the distribution gen- erating the test (target) data differs from the one generating the learning (source) data. It is well…
PAC-Bayesian Majority Vote for Late Classifier Fusion
Emilie Morvant, Amaury Habrard, Stéphane Ayache
A lot of attention has been devoted to multimedia indexing over the past few years. In the literature, we often consider two kinds of fusion schemes: The early fusion and the late…
PAC-Bayesian Generalization Bound on Confusion Matrix for Multi-Class Classification
Emilie Morvant, Sokol Koço, Liva Ralaivola
In this work, we propose a PAC-Bayes bound for the generalization risk of the Gibbs classifier in the multi-class classification framework. The novelty of our work is the critical…