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Description
  • In data mining, artificial neural networks have become one of important competitors of traditional statistical methods. They increase the potential of discovering useful knowledge in data, but only if the differences between both kinds of methods are well understood. Basic concepts od a framework based on observational logic are outlined, and it is explained, how generalized quantifiers can be defined in an observational calculus to capture data mining with statistical and ANN-based methods.
  • In data mining, artificial neural networks have become one of important competitors of traditional statistical methods. They increase the potential of discovering useful knowledge in data, but only if the differences between both kinds of methods are well understood. Basic concepts od a framework based on observational logic are outlined, and it is explained, how generalized quantifiers can be defined in an observational calculus to capture data mining with statistical and ANN-based methods. (en)
Title
  • Observational Logic Integrates Data Mining Based on Statistical and Neural Networks.
  • Observational Logic Integrates Data Mining Based on Statistical and Neural Networks. (en)
skos:prefLabel
  • Observational Logic Integrates Data Mining Based on Statistical and Neural Networks.
  • Observational Logic Integrates Data Mining Based on Statistical and Neural Networks. (en)
skos:notation
  • RIV/67985807:_____/00:06000226!RIV/2003/AV0/A06003/N
http://linked.open.../vavai/riv/strany
  • 440;445
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  • P(GA201/00/1489), Z(AV0Z1030915)
http://linked.open...vai/riv/dodaniDat
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  • 720063
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  • RIV/67985807:_____/00:06000226
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  • N/A (en)
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  • [A300BAC40249]
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  • Lyon [FR]
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  • Berlin
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  • Principles of data Mining and Knowledge Discovery.
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
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http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Holeňa, Martin
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http://linked.open...n/vavai/riv/zamer
number of pages
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  • Springer-Verlag
https://schema.org/isbn
  • 3-540-41066-X
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