Towards Stream Learning on Embedded Systems: Benchmarking the Memory Consumption of Stream Learning Methods

arXiv:2608.30923 · cs.LG, cs.AI, cs.PF · Submitted 2026-08-31 · Read on arXiv

cs.LG, cs.AI, cs.PF

Submitted: 2026-08-31

Updated: 2026-08-31

Terminology

• Accuracy — 17× in this paper · explained in 1 episode
• Adaptation — 12× in this paper · explained in 1 episode
• Concept Drift — 9× in this paper · explained in 4 episodes
• Benchmark — 8× in this paper · explained in 2 episodes
• Failure Modes — 4× in this paper · explained in 3 episodes
• Core — 3× in this paper · explained in 2 episodes
• Pruning — 3× in this paper · explained in 4 episodes
• Abstraction — 2× in this paper · explained in 1 episode
• Benchmarking — 2× in this paper · explained in 4 episodes
• Non-stationarity — 2× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• IEEE International Conference on Data Mining — 1× in this paper
• Quantization — 1× in this paper · explained in 12 episodes
• Resource Constraints — 1× in this paper · explained in 3 episodes

Sources

• CapyMOA: Efficient Machine Learning for Data Streams and Online Continual Learning in Python
• Training Machine Learning models at the Edge: A Survey

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