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@inproceedings{490371, author = {Žižka, Jan and Mádr, Michal}, address = {Hyderabad, India}, booktitle = {Proceedings of the First Indian International Conference on Artificial Intelligence (IICAI-03)}, keywords = {pattern recognition; decision trees; classification; representation of examples; relevant attributes}, language = {eng}, location = {Hyderabad, India}, isbn = {0-9727412-0-8}, pages = {1374-1387}, publisher = {IICAI-03}, title = {Learning Representative Patterns From Real Chess Positions: A Case Study}, year = {2003} }
TY - JOUR ID - 490371 AU - Žižka, Jan - Mádr, Michal PY - 2003 TI - Learning Representative Patterns From Real Chess Positions: A Case Study PB - IICAI-03 CY - Hyderabad, India SN - 0972741208 KW - pattern recognition KW - decision trees KW - classification KW - representation of examples KW - relevant attributes N2 - This paper deals with a particular pattern recognition by machine learning. The patterns are specific chess positions. A computer learns if a special pattern leads to a winning or losing game, i.e., a classification task based on real results and examples. As a learning algorithm, decision trees generated by the program C5/See5, also with boosting, were used. This algorithm does not employ chess rules or calculations of positions, it just learns from a selected set of 450 training positive and negative examples with 8 different representations of real positions played by human players. The most accurate classification reaches 92.98% for the combination of automatically generated trivial descriptions of positions (64 attributes) with expert descriptions suggested by humans (92 attributes). ER -
ŽIŽKA, Jan and Michal MÁDR. Learning Representative Patterns From Real Chess Positions: A Case Study. In \textit{Proceedings of the First Indian International Conference on Artificial Intelligence (IICAI-03)}. Hyderabad, India: IICAI-03, 2003, p.~1374-1387. ISBN~0-9727412-0-8.
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