CS 761 Spring 2013 Advanced Machine Learning Statistical Learning Theory


Consider a family of binary classifiers G = {g : X → {−1, 1}}. G can be either probabilistic models or not, such as decision trees, neural nets, SVMs, logic rules, the groundhog family of Punxsutawney Phil, a tank full of Paul the Octopus' relatives, etc. Each g ∈ G predicts the label y = g(x) from input x. Importantly, assume an unknown but fixed joint… (More)


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