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Empirically Estimable Classification Bounds Based on a Nonparametric Divergence Measure
Information divergence functions play a critical role in statistics and information theory. In this paper we show that a non-parametric f-divergence measure can be used to provide improved bounds on the minimum binary classification probability of error for the case when the training and test data a...
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| Publié dans: | IEEE Trans Signal Process |
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| Auteurs principaux: | , , , |
| Format: | Artigo |
| Langue: | Inglês |
| Publié: |
2016
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| Sujets: | |
| Accès en ligne: | https://ncbi.nlm.nih.gov/pmc/articles/PMC4717492/ https://ncbi.nlm.nih.gov/pubmed/26807014 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1109/TSP.2015.2477805 |
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