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Computational Information Geometry for Binary Classification of High-Dimensional Random Tensors †
Evaluating the performance of Bayesian classification in a high-dimensional random tensor is a fundamental problem, usually difficult and under-studied. In this work, we consider two Signal to Noise Ratio (SNR)-based binary classification problems of interest. Under the alternative hypothesis, i.e.,...
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| Publicado no: | Entropy (Basel) |
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| Main Authors: | , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
MDPI
2018
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7512719/ https://ncbi.nlm.nih.gov/pubmed/33265294 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.3390/e20030203 |
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