The afternoon of the first day of Classification and Discovery concentrated on classification methods, almost all supervised (learn with training set, run on larger data). I am largely against these methods, in part because very few of them make good use of the individual noise estimates, and in part because your training data are never the same—in important respects—as your real data. However, a nice discussion ensued, led in large part by Alexander Gray (Georgia Tech); in this I argued for generative models for classification, but of course these are only possible when you have a good model of both the Universe and your hardware!
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