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Relevance Vector Machine and Support Vector Machine Classifier Analysis of Scanning Laser Polarimetry Retinal Nerve Fiber Layer Measurements
2005
Investigative Ophthalmology and Visual Science
PURPOSE. To classify healthy and glaucomatous eyes using relevance vector machine (RVM) and support vector machine (SVM) learning classifiers trained on retinal nerve fiber layer (RNFL) thickness measurements obtained by scanning laser polarimetry (SLP). METHODS. Seventy-two eyes of 72 healthy control subjects (average age ϭ 64.3 Ϯ 8.8 years, visual field mean deviation ϭ Ϫ0.71 Ϯ 1.2 dB) and 92 eyes of 92 patients with glaucoma (average age ϭ 66.9 Ϯ 8.9 years, visual field mean deviation ϭ
doi:10.1167/iovs.04-1122
pmid:15790898
pmcid:PMC2928387
fatcat:2p2vcu54svct3itgeendgtokza