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Tutorial on Implied Posterior Probability for SVMs
Implied posterior probability of a given model (say, Support Vector Machines
(SVM)) at a point is an estimate of the class posterior probability
pertaining to the class of functions of the model applied to a given dataset.
It can be regarded as a score (or estimate) for the true posterior probability,
which can then be calibrated/mapped onto expected (non-implied by the model)
posterior probability implied by the underlying functions, which have generated
the data. In this tutorial we discuss how to compute implied posterior
probabilities of SVMs for the binary classification case as well as how to
calibrate them via a standard method of isotonic regression.Comment: 20 pages, 19 figure