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by jkabrg
2865 days ago
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I guess it provides an objective definition of when something is simple or complicated. This could have applications in statistics or machine learning, where "simpler" hypotheses could be less likely to overfit. There are computable measures of "simplicity" (unlike Kolmogorov complexity) based on compression algorithms or computation time (Levin complexity). There are also apparently connections to statistical mechanics, but I haven't looked into it and I don't know if it's useful. See this paper on how to cluster using compression: https://arxiv.org/pdf/cs/0312044.pdf |
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