. "Proceedings of the First Indian International Conference on Artificial Intelligence (IICAI-03)" . . "[1066F21E9A6C]" . . "RIV/00216224:14330/03:00009152!RIV08-MSM-14330___" . "2"^^ . . "Hyderabad, India" . "Hyderabad, India" . . . . "2"^^ . "Learning Representative Patterns From Real Chess Positions: A Case Study" . "Learning Representative Patterns From Real Chess Positions: A Case Study"@en . "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 humas (92 attributes)." . "613301" . "IICAI-03" . "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 humas (92 attributes)."@en . "M\u00E1dr, Michal" . "1374-1387" . . "Learning Representative Patterns From Real Chess Positions: A Case Study"@cs . . . "pattern recognition; decision trees; classification; representation of examples; relevant attributes"@en . . "Z(MSM 143300003)" . "Learning Representative Patterns From Real Chess Positions: A Case Study"@en . . "2003-01-01+01:00"^^ . "Learning Representative Patterns From Real Chess Positions: A Case Study" . "0-9727412-0-8" . "Learning Representative Patterns From Real Chess Positions: A Case Study"@cs . . . . . "14"^^ . "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)."@cs . "\u017Di\u017Eka, Jan" . "RIV/00216224:14330/03:00009152" . . . . "14330" .