I have some questions concernig this paper of Remi: http://remi.coulom.free.fr/Amsterdam2007/MMGoPatterns.pdf
1. Which sense make the prior (Section 3.3 in the paper and where is the application? I understand it the way that you put 2 more competitions to each pattern in the minorization-maximization formula. But how does this produce a probability distribution with mean 0 and standard deviation 302?? Is the prior only necessary to make sure that every pattern have at least one win? 2. I had run the algorithm on 400 games (including handicap-games) from the same game-records source Remi used (Section 3.2), but i used an other month. I concidered only 3x3 shape-patterns and simple non-shape pattern including dist-to-boarder, dist-to-prevMove, atari, capture, extension.. After 1 iterations of the algorithm, I got strenght-parameter values completely different to the results of the paper (Table 1). Most of my parameters (including all the dist-to-boarder parameters!) have values less than 1.0. Does anyone have some explanations on this? After 5 iteration it is even worse. Most of the low values (less than 1.0) gets even lower, the high values even higher. @Remi: How many iterations you had used? Anyone of you have similar or other experiences with the algorithm? Lars _______________________________________________ computer-go mailing list computer-go@computer-go.org http://www.computer-go.org/mailman/listinfo/computer-go/
