Glaucomatous Patterns in Frequency Doubling Technology (FDT) Perimetry Data Identified by Unsupervised Machine Learning Classifiers

Lignende værker

• Glaucomatous patterns in Frequency Doubling Technology (FDT) perimetry data identified by unsupervised machine learning classifiers.
  af: Christopher Bowd, et al.
  Udgivet: (2014-01-01)
• Unsupervised Gaussian Mixture-Model With Expectation Maximization for Detecting Glaucomatous Progression in Standard Automated Perimetry Visual Fields
  af: Yousefi, Siamak, et al.
  Udgivet: (2016)
• Predicting Glaucomatous Progression in Glaucoma Suspect Eyes Using Relevance Vector Machine Classifiers for Combined Structural and Functional Measurements
  af: Bowd, Christopher, et al.
  Udgivet: (2012)
• Learning From Data: Recognizing Glaucomatous Defect Patterns and Detecting Progression From Visual Field Measurements
  af: Yousefi, Siamak, et al.
  Udgivet: (2014)
• Progression of Patterns (POP): A Machine Classifier Algorithm to Identify Glaucoma Progression in Visual Fields
  af: Goldbaum, Michael H., et al.
  Udgivet: (2012)