A DQN-based reinforcement learning agent that dynamically chooses sliding window sizes is claimed to improve classification accuracy on multi-dimensional streams, but the reported evaluation is inconsistent and not reproducible.
Effects of Sliding Window Variation in the Performance of Acceleration-Based Human Activity Recognition Using Deep Learning Mod- els
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Designing Adaptive Algorithms Based on Reinforcement Learning for Dynamic Optimization of Sliding Window Size in Multi-Dimensional Data Streams
A DQN-based reinforcement learning agent that dynamically chooses sliding window sizes is claimed to improve classification accuracy on multi-dimensional streams, but the reported evaluation is inconsistent and not reproducible.