sorry to self-reply, but:
> alternatively, it does sphere packing over the direct product of open
> or closed (but bounded) intervals and discrete sets, so you can get a
> set of points that is slightly better than a random set of experiments
> (i.e. guaranteed to cover the space well).
arguably
> That doesn't seem to directly support deriving information from random
> trials. For computer go tuning, would you play multiple games with each
> parameter set in order to get a meaningful figure? That seems likely to
> be less efficient than treating it as a bandit problem.
you'd decide how ma
On Thu, Nov 26, 2009 at 12:06, Alain Baeckeroot
wrote:
> Maybe have a look at signal processing, using higher-orders statistics ?
> mean
> std-deviation = order 2 (or 1 ?)
> ...
>
> win by 10 with std = 100 seems much less secure than win by 5 with std=1
> but maybe this is included in modern
Le 26/11/2009 à 10:08, Vlad Dumitrescu a écrit :
>
> On Thu, Nov 26, 2009 at 00:43, Darren Cook wrote:
> > When I read this it reminded me of experiments I tried before to pass
> > more than one piece of information up from the leaf nodes of a (min-max)
> > tree. E.g. a territory estimate and an
Hi!
On Wed, Nov 25, 2009 at 01:16:02PM +0200, Steve Kroon wrote:
> I hope to have a student for the next month or two who can look into some
> computer Go before starting his Masters degree. He is interested in using
> CUDA
> for his Masters, so I thought it would be nice for him to investigat
On Thu, Nov 26, 2009 at 00:43, Darren Cook wrote:
> When I read this it reminded me of experiments I tried before to pass
> more than one piece of information up from the leaf nodes of a (min-max)
> tree. E.g. a territory estimate and an influence estimate. I gave up as
> it got too complex to han
