Author: Michael Yee
Date: 12:47:39 03/10/04
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On March 09, 2004 at 17:48:19, Gian-Carlo Pascutto wrote: >On March 09, 2004 at 16:37:46, Michael Yee wrote: > >>Wow. I was partly just being facetious with my initial comments. But I actually >>was mostly serious. I certainly don't think the analogy is "completely" flawed >>(since I think that would invalidate a lot of reasonable ideas in machine >>learning). > >Machine learning is not flawed, your analogy of what he's doing to what >you're talking about is! > >>If you parameterized your whole program, I don't see why a global search >>technique couldn't find the same weights that you hand-coded or even better ones >>(given a nice large training set). For example, let f(x) = DS's performance in a >>tournament given param vector x. Then a search technique (e.g., tabu search) >>could be used to optimize f(x) over x. I admit that it could take a long time, >>but I don't think it's impossible. (Also, I think it would still work if f(x) >>was based on the ability to match GM moves form a large set of training >>positions.) > >Yes, this would work. No, this isn't what he's doing. He's making changes in >(the parameters of) one part of the program, and then measuring something >entirely different. > >Strength of evaluation and the results of a tactical testset are not correlated >enough to be useful for optimalisation. As he already noticed in his first post, >they can be negatively correlated, even. > >-- >GCP Hi Gian-Carlo, You're right--I wasn't really referring to what the first poster had tried to do. Instead, I was responding to your general statement: "Tuning your evaluation on a testsuite is fundamentally wrong." I just wanted to give some examples of approaches (i.e., parameter estimation based on training examples) that seem reasonable to me. In other words, I wanted to qualify your statement a bit. If I ever get positive results (similar to some already existing in papers that Dave hinted at), I'll be sure to share them. Michael
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