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Description
  • Publikace je krátkým přehledem a srovnáním dvou pravděpodobnostních přístupů k reprezentaci znalostí: bayesovských sítí a kompozicionálních modelů. Tyto modely byly zvoleny jedna proto, že reprezentují stejnou třídu distribucí, dále pak proto, že jsou typickými představiteli přístupů používajících podmíněnou (bayesovské sítě) či nepodmíněnou (kompozicionální modely) distribuci jako základní stavební kámen modelu. Porovnání je provedeno především z hlediska zpracování částečných znalostí. (cs)
  • The paper is a short review and comparison of two probabilistic models for uncertain knowledge representation: Bayesian networks and compositional models. These two approaches were chosen because they represent the same class of distributions and because they are typical representatives of the approaches using conditional (for Bayesian networks) and unconditional (for compositional models) distributions as basic building blocks for model construction. The comparison is made from the viewpoint of partial knowledge processing, in particular. Here we have in mind not only their capability to create global models from systems of pieces of local knowledge but most of all their efficiency to infer new pieces of local knowledge, different from those forming a generating (input) system.
  • The paper is a short review and comparison of two probabilistic models for uncertain knowledge representation: Bayesian networks and compositional models. These two approaches were chosen because they represent the same class of distributions and because they are typical representatives of the approaches using conditional (for Bayesian networks) and unconditional (for compositional models) distributions as basic building blocks for model construction. The comparison is made from the viewpoint of partial knowledge processing, in particular. Here we have in mind not only their capability to create global models from systems of pieces of local knowledge but most of all their efficiency to infer new pieces of local knowledge, different from those forming a generating (input) system. (en)
Title
  • Probabilistic partial knowledge handling
  • Pravděpodobnostní metody práce s částečnými znalostmi (cs)
  • Probabilistic partial knowledge handling (en)
skos:prefLabel
  • Probabilistic partial knowledge handling
  • Pravděpodobnostní metody práce s částečnými znalostmi (cs)
  • Probabilistic partial knowledge handling (en)
skos:notation
  • RIV/67985556:_____/08:00098606!RIV08-AV0-67985556
http://linked.open.../vavai/riv/strany
  • 359;367
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(1M0572), P(2C06019), P(IAA2075302), Z(AV0Z10750506)
http://linked.open...iv/cisloPeriodika
  • 3
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 389732
http://linked.open...ai/riv/idVysledku
  • RIV/67985556:_____/08:00098606
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Knowledge representation; Bayesian network; Operator of composition; Consistence (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...odStatuVydavatele
  • US - Spojené státy americké
http://linked.open...ontrolniKodProRIV
  • [2D8A8E3D9551]
http://linked.open...i/riv/nazevZdroje
  • International Journal of Approximate Reasoning
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...v/svazekPeriodika
  • 47
http://linked.open...iv/tvurceVysledku
  • Jiroušek, Radim
http://linked.open...n/vavai/riv/zamer
issn
  • 0888-613X
number of pages
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