Using unsupervised learning with independent component analysis to identify patterns of glaucomatous visual field defects.
暂无分享,去创建一个
Robert N Weinreb | Christopher Bowd | Jiucang Hao | Te-Won Lee | Kwokleung Chan | Catherine Boden | Pamela A Sample | Michael H Goldbaum | Terrence Sejnowski | Zuohua Zhang | Rupert Bourne | Linda Zangwill | David Spinak | T. Sejnowski | Te-Won Lee | M. Goldbaum | R. Bourne | L. Zangwill | R. Weinreb | C. Bowd | Zuohua Zhang | J. Hao | P. Sample | C. Boden | D. Spinak | Kwokleung Chan
[1] M H Goldbaum,et al. Interpretation of automated perimetry for glaucoma by neural network. , 1994, Investigative ophthalmology & visual science.
[2] David Mackay,et al. Probable networks and plausible predictions - a review of practical Bayesian methods for supervised neural networks , 1995 .
[3] Terrence J. Sejnowski,et al. ICA Mixture Models for Unsupervised Classification of Non-Gaussian Classes and Automatic Context Switching in Blind Signal Separation , 2000, IEEE Trans. Pattern Anal. Mach. Intell..
[4] Robert N Weinreb,et al. Using unsupervised learning with variational bayesian mixture of factor analysis to identify patterns of glaucomatous visual field defects. , 2004, Investigative ophthalmology & visual science.
[5] D B Henson,et al. Spatial classification of glaucomatous visual field loss. , 1996, The British journal of ophthalmology.
[6] L Brigatti,et al. Neural networks to identify glaucoma with structural and functional measurements. , 1996, American journal of ophthalmology.
[7] 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.
[8] T. Sejnowski,et al. Unsupervised machine learning with independent component analysis to identify areas of progression in glaucomatous visual fields. , 2005, Investigative ophthalmology & visual science.
[9] Robert N Weinreb,et al. Comparing machine learning classifiers for diagnosing glaucoma from standard automated perimetry. , 2002, Investigative ophthalmology & visual science.
[10] Robert N Weinreb,et al. Using machine learning classifiers to identify glaucomatous change earlier in standard visual fields. , 2002, Investigative ophthalmology & visual science.
[11] Terrence J. Sejnowski,et al. Variational Learning of Clusters of Undercomplete Nonsymmetric Independent Components , 2003, J. Mach. Learn. Res..
[12] G. Trick,et al. Assessing the utility of reliability indices for automated visual fields. Testing ocular hypertensives. , 1989, Ophthalmology.