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SVM and Pattern-Enriched Common Fate Graphs for the Game of Go
Liva Ralaivola, Lin Wu and Pierre Baldi
In: ESANN 2005, 27-29 Apr 2005, Bruges, Belgium.
Abstract We propose a pattern-based approach combined with the concept of
Enriched Common Fate Graph for 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.
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