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Found 4 papers, 4 papers with code
Dynamic Ensemble Size Adjustment for Memory Constrained Mondrian Forest
Supervised learning algorithms generally assume the availability of enough memory to store data models during the training and test phases.
Mondrian Forest for Data Stream Classification Under Memory Constraints
Supervised learning algorithms generally assume the availability of enough memory to store their data model during the training and test phases.
Reducing numerical precision preserves classification accuracy in Mondrian Forests
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.
A benchmark of data stream classification for human activity recognition on connected objects
We measure both classification performance and resource consumption (runtime, memory, and power) of five usual stream classification algorithms, implemented in a consistent library, and applied to two real human activity datasets and to three synthetic datasets.
