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Statements

Subject Item
n2:RIV%2F00216224%3A14330%2F03%3A00009152%21RIV08-MSM-14330___
rdf:type
skos:Concept n8:Vysledek
dcterms: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). 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).
dcterms:title
Learning Representative Patterns From Real Chess Positions: A Case Study Learning Representative Patterns From Real Chess Positions: A Case Study Learning Representative Patterns From Real Chess Positions: A Case Study
skos:prefLabel
Learning Representative Patterns From Real Chess Positions: A Case Study Learning Representative Patterns From Real Chess Positions: A Case Study Learning Representative Patterns From Real Chess Positions: A Case Study
skos:notation
RIV/00216224:14330/03:00009152!RIV08-MSM-14330___
n4:strany
1374-1387
n4:aktivita
n17:Z
n4:aktivity
Z(MSM 143300003)
n4:dodaniDat
n19:2008
n4:domaciTvurceVysledku
n12:9299483 n12:6219810
n4:druhVysledku
n14:D
n4:duvernostUdaju
n20:S
n4:entitaPredkladatele
n7:predkladatel
n4:idSjednocenehoVysledku
613301
n4:idVysledku
RIV/00216224:14330/03:00009152
n4:jazykVysledku
n9:eng
n4:klicovaSlova
pattern recognition; decision trees; classification; representation of examples; relevant attributes
n4:klicoveSlovo
n5:classification n5:pattern%20recognition n5:decision%20trees n5:representation%20of%20examples n5:relevant%20attributes
n4:kontrolniKodProRIV
[1066F21E9A6C]
n4:mistoKonaniAkce
Hyderabad, India
n4:mistoVydani
Hyderabad, India
n4:nazevZdroje
Proceedings of the First Indian International Conference on Artificial Intelligence (IICAI-03)
n4:obor
n6:IN
n4:pocetDomacichTvurcuVysledku
2
n4:pocetTvurcuVysledku
2
n4:rokUplatneniVysledku
n19:2003
n4:tvurceVysledku
Mádr, Michal Žižka, Jan
n4:typAkce
n13:WRD
n4:zahajeniAkce
2003-01-01+01:00
n4:zamer
n16:MSM%20143300003
s:numberOfPages
14
n11:hasPublisher
IICAI-03
n15:isbn
0-9727412-0-8
n21:organizacniJednotka
14330