= Cox–Zucker machine
{wiki=Cox–Zucker_machine}
The Cox–Zucker machine is a theoretical construct related to computational learning theory and reinforcement learning. Named after statisticians David R. Cox and Herbert Zucker, it often refers to a model or framework that has applications in understanding the behavior of algorithms and systems that learn from data over time. While specific details about the Cox–Zucker machine might not be extensively documented in widely available literature, it typically involves aspects of statistical modeling and inference that are relevant to machine learning processes.
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