[Computer-go] CLOP: Confident Local Optimization forNoisyBlack-Box Parameter Tuning

[Rémi Coulom]

Well, that exponential is a Gaussian when q is definite negative (which is often the case). But I see what you mean.

On 10 sept. 2011, at 18:47, Brian Sheppard wrote:

> Yes, that makes sense. You don't want Gaussian there. 
> 
> -----Original Message-----
> From: computer-go-bounces at dvandva.org
> [mailto:computer-go-bounces at dvandva.org] On Behalf Of Rémi Coulom
> Sent: Saturday, September 10, 2011 11:36 AM
> To: computer-go at dvandva.org
> Subject: Re: [Computer-go] CLOP: Confident Local Optimization
> forNoisyBlack-Box Parameter Tuning
> 
> 
> 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 
>> understand.
>> 
>> 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)?
>> 
>> Thanks,
>> Brian
> 
> 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:
> http://remi.coulom.free.fr/CLOP/
> 
> Rémi
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