Title :
Creating an SVM to play strong poker
Author :
Blank, Tanya ; Leen-Kiat Soh ; Scott, Simon
Author_Institution :
Computer Science Department, University of Nebraska-Lincoln, Lincoln, NE, U.S.A
Abstract :
We present a support vector machine that plays strong poker based on training on data from a proven player. This approach allows us to create an agent without having to use and implement expert rules. We tested our support vector machine by having it play against several opponents. It did not perform as well as expected, but did show some promise.
Keywords :
Artificial neural networks; Computer science; Humans; Internet; Kernel; Pattern recognition; Support vector machine classification; Support vector machines; Testing;
Conference_Titel :
Machine Learning and Applications, 2004. Proceedings. 2004 International Conference on
Conference_Location :
Louisville, Kentucky, USA
Print_ISBN :
0-7803-8823-2
DOI :
10.1109/ICMLA.2004.1383507
