1 citations · 1 across the 2 of their papers we have counts for
3 papers
cs.LG2026
Analytical study of the optimal combination of binary classifiers based on classifiers-induced partitioning of the training set
Jean-Marc Brossier, Olivier Lafitte
This paper studies an optimal linear combination of binary classifiers based on a logical structuration of the dataset via truth tables. The given classifiers partition data into e…
cs.LG2023
When Analytic Calculus Cracks AdaBoost Code
Jean-Marc Brossier, Olivier Lafitte, Lenny Réthoré
The principle of boosting in supervised learning involves combining multiple weak classifiers to obtain a stronger classifier. AdaBoost has the reputation to be a perfect example o…
cs.IT2012★ 1 cited
Adaptive Quantizers for Estimation
Rodrigo Cabral Farias, Jean-Marc Brossier
In this paper, adaptive estimation based on noisy quantized observations is studied. A low complexity adaptive algorithm using a quantizer with adjustable input gain and offset is…