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Mutual Information Gain and Linear/Nonlinear Redundancy for Agent Learning, Sequence Analysis, and Modeling
In many applications, intelligent agents need to identify any structure or apparent randomness in an environment and respond appropriately. We use the relative entropy to separate and quantify the presence of both linear and nonlinear redundancy in a sequence and we introduce the new quantities of t...
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| Pubblicato in: | Entropy (Basel) |
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| Autore principale: | |
| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
MDPI
2020
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| Soggetti: | |
| Accesso online: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7517148/ https://ncbi.nlm.nih.gov/pubmed/33286380 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.3390/e22060608 |
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