Relevance vector machine and support vector machine classifier analysis of scanning laser polarimetry retinal nerve fiber layer measurements.
about
Optic nerve head and fibre layer imaging for diagnosing glaucomaGlaucoma Diagnostic Accuracy of Machine Learning Classifiers Using Retinal Nerve Fiber Layer and Optic Nerve Data from SD-OCT.Scanning laser polarimetry with variable and enhanced corneal compensation in normal and glaucomatous eyes.Optic nerve head and retinal nerve fiber layer analysis: a report by the American Academy of Ophthalmology.Comparison of retinal nerve fiber layer and optic disc imaging for diagnosing glaucoma in patients suspected of having the diseaseScanning laser polarimetry: logistic regression analysis for perimetric glaucoma diagnosis.Diagnostic tools for glaucoma detection and management.Prediction of human functional genetic networks from heterogeneous data using RVM-based ensemble learningCombining functional and structural tests improves the diagnostic accuracy of relevance vector machine classifiersPredicting glaucomatous progression in glaucoma suspect eyes using relevance vector machine classifiers for combined structural and functional measurementsEvaluation of machine learning classifiers in keratoconus detection from orbscan II examinations.Glaucoma progression detection using structural retinal nerve fiber layer measurements and functional visual field pointsPattern recognition can detect subtle field defects in eyes of HIV individuals without retinitis under HAART.Glaucomatous patterns in Frequency Doubling Technology (FDT) perimetry data identified by unsupervised machine learning classifiersMachine learning classifiers detect subtle field defects in eyes of HIV individuals.Combining information from 3 anatomic regions in the diagnosis of glaucoma with time-domain optical coherence tomography.Unsupervised Gaussian Mixture-Model With Expectation Maximization for Detecting Glaucomatous Progression in Standard Automated Perimetry Visual Fields.Assessing visual field clustering schemes using machine learning classifiers in standard perimetry.
P2860
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P2860
Relevance vector machine and support vector machine classifier analysis of scanning laser polarimetry retinal nerve fiber layer measurements.
description
2005 nî lūn-bûn
@nan
2005 թուականի Ապրիլին հրատարակուած գիտական յօդուած
@hyw
2005 թվականի ապրիլին հրատարակված գիտական հոդված
@hy
2005年の論文
@ja
2005年論文
@yue
2005年論文
@zh-hant
2005年論文
@zh-hk
2005年論文
@zh-mo
2005年論文
@zh-tw
2005年论文
@wuu
name
Relevance vector machine and s ...... erve fiber layer measurements.
@ast
Relevance vector machine and s ...... erve fiber layer measurements.
@en
type
label
Relevance vector machine and s ...... erve fiber layer measurements.
@ast
Relevance vector machine and s ...... erve fiber layer measurements.
@en
prefLabel
Relevance vector machine and s ...... erve fiber layer measurements.
@ast
Relevance vector machine and s ...... erve fiber layer measurements.
@en
P2093
P2860
P50
P356
P1476
Relevance vector machine and s ...... erve fiber layer measurements.
@en
P2093
Felipe A Medeiros
Jiucang Hao
Michael H Goldbaum
Te-Won Lee
Zuohua Zhang
P2860
P304
P356
10.1167/IOVS.04-1122
P407
P577
2005-04-01T00:00:00Z