MM and CLOP are completely different from each other. MM is for supervised learning of a playing policy from game records. It will tune parameters (pattern weights) to match a given set of samples moves (typically from a collection of game records of strong players).
CLOP is black-box optimization, that is to say it can optimize the win rate of your program by letting your program play games against a reference opponent. It will try plenty of different parameter values, play games with them, and try to estimate the best value for the parameter from that. They both optimize something: MM optimizes how the playing policy matches some given sample moves, and CLOP optimizes the win rate against a reference opponent. But you cannot use one method to do the job of the other. Rémi On 11 févr. 2014, at 20:42, Peter Drake <[email protected]> wrote: > A naive question: > > In what situations is it better to use Coulom's Elo method vs his CLOP method > for setting parameters? It seems they are both techniques for optimizing a > high-dimensional, noisy function. > > -- > Peter Drake > https://sites.google.com/a/lclark.edu/drake/ > _______________________________________________ > Computer-go mailing list > [email protected] > http://dvandva.org/cgi-bin/mailman/listinfo/computer-go _______________________________________________ Computer-go mailing list [email protected] http://dvandva.org/cgi-bin/mailman/listinfo/computer-go
