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- W2080149324 abstract "In networked video stream mining systems, real-time video contents are captured remotely and, subsequently, encoded and transmitted over bandwidth-constrained networks for classification at the receiver. One key task at the encoder is to adapt its compression on the fly based on time-varying network bandwidth and video characteristics — while attaining low delay and high classification accuracy. In this paper, we formalize the decision at the encoder side as an infinite horizon Markov Decision Process (MDP). We employ low-complexity, model-free reinforcement learning schemes to solve this problem efficiently under dynamic and unknown environment. Our proposed scheme adopts the technique of virtual experience (VE) update to drastically speed up convergence over conventional Q-learning, allowing the encoder to react to abrupt network changes on the order of minutes, instead of hours. In comparison to myopic optimization, it consistently achieves higher overall reward and lower sending delay under various network conditions." @default.
- W2080149324 created "2016-06-24" @default.
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- W2080149324 date "2013-07-01" @default.
- W2080149324 modified "2023-09-23" @default.
- W2080149324 title "Low-complexity reinforcement learning for delay-sensitive compression in networked video stream mining" @default.
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- W2080149324 doi "https://doi.org/10.1109/icme.2013.6607600" @default.
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