About: Learning Representative Patterns From Real Chess Positions: A Case Study

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rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
Description	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). 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) 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)
Title	Learning Representative Patterns From Real Chess Positions: A Case Study Learning Representative Patterns From Real Chess Positions: A Case Study (en) Learning Representative Patterns From Real Chess Positions: A Case Study (cs)
skos:prefLabel	Learning Representative Patterns From Real Chess Positions: A Case Study Learning Representative Patterns From Real Chess Positions: A Case Study (en) Learning Representative Patterns From Real Chess Positions: A Case Study (cs)
skos:notation	RIV/00216224:14330/03:00009152!RIV08-MSM-14330___
http://linked.open.../vavai/riv/strany	1374-1387
http://linked.open...avai/riv/aktivita	Z
http://linked.open...avai/riv/aktivity	Z(MSM 143300003)
http://linked.open...vai/riv/dodaniDat	2008
http://linked.open...aciTvurceVysledku	Mádr, Michal Žižka, Jan
http://linked.open.../riv/druhVysledku	D - Článek ve sborníku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	Masarykova univerzita / Fakulta informatiky
http://linked.open...dnocenehoVysledku	613301
http://linked.open...ai/riv/idVysledku	RIV/00216224:14330/03:00009152
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	pattern recognition; decision trees; classification; representation of examples; relevant attributes (en)
http://linked.open.../riv/klicoveSlovo	decision trees classification pattern recognition relevant attributes representation of examples
http://linked.open...ontrolniKodProRIV	[1066F21E9A6C]
http://linked.open...v/mistoKonaniAkce	Hyderabad, India
http://linked.open...i/riv/mistoVydani	Hyderabad, India
http://linked.open...i/riv/nazevZdroje	Proceedings of the First Indian International Conference on Artificial Intelligence (IICAI-03)
http://linked.open...in/vavai/riv/obor	IN
http://linked.open...ichTvurcuVysledku	2 (xsd:int)
http://linked.open...cetTvurcuVysledku	2 (xsd:int)
http://linked.open...UplatneniVysledku	2003
http://linked.open...iv/tvurceVysledku	Žižka, Jan Mádr, Michal
http://linked.open...vavai/riv/typAkce	WRD - Světová
http://linked.open.../riv/zahajeniAkce	2003-01-01 (xsd:date)
http://linked.open...n/vavai/riv/zamer	http://linked.opendata.cz/resource/domain/vavai/zamer/MSM%20143300003
number of pages	14 (xsd:int)
http://purl.org/ne...btex#hasPublisher	IICAI-03
https://schema.org/isbn	0-9727412-0-8
http://localhost/t...ganizacniJednotka	14330

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