A Brief Note on Maximum Realisable Mcmc Classifiers
نویسندگان
چکیده
We present a novel and powerful strategy for estimating and combining classi ers via ROC curves, decision analysis theory and MCMC simulation. This paradigm also allows us to select samples from an MCMC run in a parsimonious and optimal fashion. Each ROC curve, corresponds to a sample (classi er) obtained with a full Bayesian model, which treats the model dimension, model parameters, regularisation parameters and noise parameters as random variables. These variables are computed with the reversible jump MCMC algorithm. We use the fact that the ROC convex hull is the maximum realisable classi er to combine and select the best classi ers. i
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