SVM and pattern-enriched common fate graphs for the game of go
We propose a pattern-based approach combined with the concept of Enriched Common Fate Graphfor the problem of classifying Go positions. A kernel function for weighted graphs to compute the similarity between two board positions is proposed and used to learn a support vector machine and address the problem of position evaluation. Numerical simulations are carried out using a set of human played games and show the relevance of our approach.