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http://dx.doi.org/10.7583/JKGS.2013.13.2.59

Applying Principal Component Analysis to Go Openings  

Lee, Byung-Doo (Division of Sports Science, Sehan University)
Park, Jong-Wook (Division of Sports Science, Sehan University)
Publication Information
Journal of Korea Game Society / v.13, no.2, 2013 , pp. 59-70 More about this Journal
Abstract
Although the history of the game of Go is more than 2,500 years, the theoretical studies of Go are still insufficient. In recent years a lot of studies using Artificial Intelligent(AI) have been conducted, but they do not provide the prominent theoretical reality. We applied Principal Component Analysis(PCA) to the professional Go openings, which are the early stage in Go, to analyze them especially focused on the Go game records of the professional 9-dan player Lee Sedol who is the world's top professional Go player. The results showed that among the 361 eigenvectors the 48 most significant eigenvectors capture most of the variance (99.9%) and the 30 most significant eigenvectors enable to possess 90.5 percent of the total variance. This result would be expected to considerably contribute to pattern recognition research of the professional Go openings in the near future.
Keywords
Go openings; Principal Component Analysis;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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