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Unsupervised Machine Learning with Independent Component Analysis to Identify Areas of Progression in Glaucomatous Visual Fields
Purpose. To determine whether a variational Bayesian independent component analysis mixture model (vB-ICA-mm), a form of unsupervised machine learning, can be used to identify and quantify areas of progression in standard automated perimetry fields. Methods. In an earlier study, it was shown that a...
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| Autori principali: | , , , , , , , , , , , |
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| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
2005
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| Soggetti: | |
| Accesso online: | https://ncbi.nlm.nih.gov/pmc/articles/PMC1832121/ https://ncbi.nlm.nih.gov/pubmed/16186350 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1167/iovs.04-1168 |
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