[Computer-go] CLOP: Confident Local Optimization for NoisyBlack-Box Parameter Tuning
Remi.Coulom at free.fr
Sat Sep 10 08:36:27 PDT 2011
On 10 sept. 2011, at 17:20, Brian Sheppard wrote:
> I am going through the paper, and there is a point where I do not
> When the weights are recalculated in Algorithm 1, the expression for wk is
> exp((qk(x) - mk) / H * sk).
> Should the formula have a square? That is, exp((qk(x) - mk) * (qk(x) - mk) /
> H * sk)?
No. The idea is that the weight of a sample should be low when it is far below the mean, not when it is far from the mean. That is to say, samples whose value is very low according to the regression get a low weight. But samples whose strength is estimated to be above average keep a full weight of 1 (because of the "min", the weight can never get above 1).
Note BTW that since my previous message I updated the web site of CLOP with some data, screenshots, and a link to the computer-chess forum with more discussions about the algorithm:
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