Relevance vector machine and support vector machine classifier analysis of scanning laser polarimetry retinal nerve fiber layer measurements.
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T. Sejnowski | Te-Won Lee | M. Goldbaum | F. Medeiros | L. Zangwill | R. Weinreb | C. Bowd | Zuohua Zhang | J. Hao
[1] Andrew P. Sage,et al. Uncertainty in Artificial Intelligence , 1987, IEEE Transactions on Systems, Man, and Cybernetics.
[2] E. DeLong,et al. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. , 1988, Biometrics.
[3] R. Weinreb,et al. Histopathologic validation of Fourier-ellipsometry measurements of retinal nerve fiber layer thickness. , 1990, Archives of ophthalmology.
[4] Douglas R. Anderson,et al. Clinical Decisions In Glaucoma , 1993 .
[5] L Brigatti,et al. Neural networks to identify glaucoma with structural and functional measurements. , 1996, American journal of ophthalmology.
[6] J. Caprioli,et al. Detection of structural damage from glaucoma with confocal laser image analysis. , 1996, Investigative ophthalmology & visual science.
[7] Vladimir Vapnik,et al. Statistical learning theory , 1998 .
[8] R. Weinreb. Evaluating the retinal nerve fiber layer in glaucoma with scanning laser polarimetry. , 1999, Archives of ophthalmology.
[9] Christopher M. Bishop,et al. Variational Relevance Vector Machines , 2000, UAI.
[10] R. Knighton,et al. Effect of corneal polarization axis on assessment of retinal nerve fiber layer thickness by scanning laser polarimetry. , 2000, American journal of ophthalmology.
[11] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[12] L. Zangwill,et al. Detecting early glaucoma by assessment of retinal nerve fiber layer thickness and visual function. , 2001, Investigative ophthalmology & visual science.
[13] H. Lemij,et al. Prevalence of split nerve fiber layer bundles in healthy eyes imaged with scanning laser polarimetry. , 2001, Ophthalmology.
[14] George Eastman House,et al. Sparse Bayesian Learning and the Relevance Vector Machine , 2001 .
[15] Robert N Weinreb,et al. Comparing machine learning classifiers for diagnosing glaucoma from standard automated perimetry. , 2002, Investigative ophthalmology & visual science.
[16] Robert N Weinreb,et al. Using machine learning classifiers to identify glaucomatous change earlier in standard visual fields. , 2002, Investigative ophthalmology & visual science.
[17] Joseph Caprioli,et al. Correction for the erroneous compensation of anterior segment birefringence with the scanning laser polarimeter for glaucoma diagnosis. , 2002, Investigative ophthalmology & visual science.
[18] Robert N Weinreb,et al. Comparing neural networks and linear discriminant functions for glaucoma detection using confocal scanning laser ophthalmoscopy of the optic disc. , 2002, Investigative ophthalmology & visual science.
[19] R. Weinreb,et al. Individualized compensation of anterior segment birefringence during scanning laser polarimetry. , 2002, Investigative ophthalmology & visual science.
[20] L. Zangwill,et al. Measurement of the magnitude and axis of corneal polarization with scanning laser polarimetry. , 2002, Archives of ophthalmology.
[21] Bagging Tree Classifiers for Laser Scanning Images: Data and Simulation Based Strategy , 2002, Artif. Intell. Medicine.
[22] Christopher M. Bishop,et al. Bayesian Regression and Classification , 2003 .
[23] Robert N Weinreb,et al. Association between scanning laser polarimetry measurements using variable corneal polarization compensation and visual field sensitivity in glaucomatous eyes. , 2003, Archives of ophthalmology.
[24] Torsten Hothorn,et al. New glaucoma classification method based on standard Heidelberg Retina Tomograph parameters by bagging classification trees. , 2003, Journal of glaucoma.
[25] F A Medeiros,et al. Comparison of algorithms for detection of localised nerve fibre layer defects using scanning laser polarimetry , 2003, The British journal of ophthalmology.
[26] L. Zangwill,et al. Glaucoma detection using scanning laser polarimetry with variable corneal polarization compensation. , 2003, Archives of ophthalmology.
[27] W. Feuer,et al. Scanning laser polarimetry with variable corneal compensation and optical coherence tomography in normal and glaucomatous eyes. , 2003, American journal of ophthalmology.
[28] F. Medeiros,et al. Optic Nerve Imaging: Recent Advances , 2004 .
[29] Robert N Weinreb,et al. Comparison of scanning laser polarimetry using variable corneal compensation and retinal nerve fiber layer photography for detection of glaucoma. , 2004, Archives of ophthalmology.
[30] T. Sejnowski,et al. Heidelberg retina tomograph measurements of the optic disc and parapapillary retina for detecting glaucoma analyzed by machine learning classifiers. , 2004, Investigative ophthalmology & visual science.
[31] D Cavouras,et al. Development of the cubic least squares mapping linear-kernel support vector machine classifier for improving the characterization of breast lesions on ultrasound. , 2004, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.
[32] H. Lemij,et al. Variable corneal compensation improves discrimination between normal and glaucomatous eyes with the scanning laser polarimeter. , 2004, Ophthalmology.
[33] D. Greenfield,et al. Quantitative assessment of structural damage in eyes with localized visual field abnormalities. , 2004, American journal of ophthalmology.
[34] M. Masotti,et al. A novel featureless approach to mass detection in digital mammograms based on support vector machines. , 2004, Physics in medicine and biology.
[35] D. Greenfield,et al. Retinal Nerve Fiber Layer Assessment Using Scanning Laser Polarimetry , 2004, International ophthalmology clinics.
[36] F. Medeiros,et al. Comparison of the GDx VCC scanning laser polarimeter, HRT II confocal scanning laser ophthalmoscope, and stratus OCT optical coherence tomograph for the detection of glaucoma. , 2004, Archives of ophthalmology.
[37] Hans G Lemij,et al. The relationship between standard automated perimetry and GDx VCC measurements. , 2004, Investigative ophthalmology & visual science.
[38] Daniel J. Strauss,et al. Objective detection of the central auditory processing disorder:A new machine learning approach , 2004, IEEE Transactions on Biomedical Engineering.
[39] A. Shoeb,et al. Patient-specific seizure onset detection , 2004, Epilepsy & Behavior.
[40] Robert N Weinreb,et al. Confocal scanning laser ophthalmoscopy classifiers and stereophotograph evaluation for prediction of visual field abnormalities in glaucoma-suspect eyes. , 2004, Investigative ophthalmology & visual science.