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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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Detalles Bibliográficos
Autores principales: Pham, Gia-Thuy, Boyer, Rémy, Nielsen, Frank
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7512719/
https://www.ncbi.nlm.nih.gov/pubmed/33265294
http://dx.doi.org/10.3390/e20030203