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Unsupervised Gaussian Mixture-Model With Expectation Maximization for Detecting Glaucomatous Progression in Standard Automated Perimetry Visual Fields
PURPOSE: To validate Gaussian mixture-model with expectation maximization (GEM) and variational Bayesian independent component analysis mixture-models (VIM) for detecting glaucomatous progression along visual field (VF) defect patterns (GEM–progression of patterns (POP) and VIM-POP). To compare GEM-...
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| 出版年: | Transl Vis Sci Technol |
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| 主要な著者: | , , , , , , , , |
| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
The Association for Research in Vision and Ophthalmology
2016
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| 主題: | |
| オンライン・アクセス: | https://ncbi.nlm.nih.gov/pmc/articles/PMC4855479/ https://ncbi.nlm.nih.gov/pubmed/27152250 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1167/tvst.5.3.2 |
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