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ref: bookmark-0 tags: machine_learning date: 0-0-2006 0:0 revision:0 [head]

http://www.iovs.org/cgi/reprint/46/4/1322.pdf

A related machine learning classifier, the relevance vector machine (RVM), has recently been introduced, which, unlike SVM, incorporates probabalistic output (probability of membership) through Bayesian inference. Its decision function depends on fewer input variables that SVM, possibly allowing better classification for small data sets with high dimensionality.

  • input data here is a number of glaucoma-correlated parameters.
  • " SVM is a machine classification method that directly minimizes the classification error without requiring a statistical data model. SVM uses a kernel function to find a hyperplane that maximizes the distance (margin) between two classes (or more?). The resultant model is spares, depending only on a few training samples (support vectors).
  • The RVM has the same functional form as the SVM within a Bayesian framework. This classifier is a sparse Bayesian model that provides probabalistic predictions (e.g. probability of glaucoma based on the training samples) through bayesian inference.
    • RVM outputs probabilities of membership rather than point estimates like SVM