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Why not use a residual sum of squares here? https://en.wikipedia.org/wiki/Residual_sum_of_squares https://en.wikipedia.org/wiki/Residual_sum_of_squares
by just2n 13y ago
Why not use a residual sum of squares here?
https://en.wikipedia.org/wiki/Residual_sum_of_squares https://en.wikipedia.org/wiki/Residual_sum_of_squares
- ColinWright 13y agoI don't understand your question. My (admittedly naive) understanding of using RSS is this. I have a model of the data, which in this case will be expected frequencies of my n-grams. Then I compute the actual frequencies of the n-grams, take the difference, square the difference, and add up all the squares. A small number is then indicative of a good fit. I don't see why this would be better. Not least, this has the problem of all those n-grams that don't turn up in my decrypt. I still have to square their expected frequency and add them into the sum. That contrasts with the method I'm using, where I just take the n-grams that do appear, multiply by the expected frequency, and add that into a running total. So I guess I don't understand your suggestion.