(no subject)

I really like the Cover and Hart result, that you can do nearest neighbor classification (classify to the category of the most similar previous observation), and asymptotically get an error rate less than twice that of the Bayes classifier (classify to the category with the highest posterior probability).

Actually, don’t think of your dataset as observations. Think of them as samples from the posterior distribution, which you generated computationally. So you can do classification by sampling from the posterior distribution, instead of by calculating posterior probability for individual given cases.

And then, sampling from the posterior distribution seems analogous to sitting around and thinking of hypothetical situations which seem plausible to you. So this is a model of reasoning that has a place for thinking, I mean, a kind of thinking which isn’t like, counting possible worlds that remain after eliminating those inconsistent with your observations. Although it doesn’t tell you precisely how much thinking you need to do or when you need more of it, so it’s not a model of reasoning that can really guide thinking? Except, maybe, “try to think of a representative sample of plausible cases”. Rather than “think of plausible cases in which your favored presidential candidate would have a positive impact”, or something.

(no subject)

(snoke + snape) / 2 = snake

(no subject)

finest-quality

reblogged your post and added:

so i think the set of matrices you’re talking about is a real thing: the orthogonals with determinant 1. The reflections have -1

(no subject)

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