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Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2013 Vol.3(1): 70-74 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2013.V3.275

Estimating Winning Probability for Texas Hold'em Poker

Wenkai Li and Lin Shang
Abstract—Among all the technologies in creating a good poker agent, estimating winning probability is a key issue. In this paper, we propose an approach to estimating winning probability for Texas Hold’em poker. We design a data structure using both the observable data from the current board and the history. A Support Vector Machine classifier is trained and 5-fold cross-validation is employed. We create a poker agent with some decision making strategies to compete. Experimental results show that our method has outperformed three other agents in precision of estimating winning probability.

Index Terms—Opponent modeling, support vector machine, texas hold’em poker, winning probability.

The authors are with the State Key Laboratory for Novel Software Technology, Department of Computer Science and Technology at the Nanjing University, Nanjing, China (e-mail: easerene@gmail.com).

[PDF]

Cite:Wenkai Li and Lin Shang, "Estimating Winning Probability for Texas Hold'em Poker," International Journal of Machine Learning and Computing vol. 3, no. 1, pp. 70-74, 2013.

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