3 papers
cs.LG2022
Dynamic Ensemble Size Adjustment for Memory Constrained Mondrian Forest
Martin Khannouz, Tristan Glatard
Supervised learning algorithms generally assume the availability of enough memory to store data models during the training and test phases. However, this assumption is unrealistic…
cs.LG2021
Reducing numerical precision preserves classification accuracy in Mondrian Forests
Marc Vicuna, Martin Khannouz, Gregory Kiar +2
Mondrian Forests are a powerful data stream classification method, but their large memory footprint makes them ill-suited for low-resource platforms such as connected objects. We e…
cs.LG2020
A benchmark of data stream classification for human activity recognition on connected objects
Martin Khannouz, Tristan Glatard
This paper evaluates data stream classifiers from the perspective of connected devices, focusing on the use case of HAR. We measure both classification performance and resource con…