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024 3 $z9781586036744
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100   $a20070117d2006      uy             0
101 0 $aeng
135   $aur|n|---|||||
181   $ctxt
182   $cc
183   $acr
200 13$aAn inductive logic programming approach to statistical relational learning$b[electronic resource] /$fKristian Kersting
210   $aAmsterdam ;$aWashington, D.C. $cIOS Press$dc2006
215   $a1 online resource (256 p.)
225 1 $aFrontiers in artificial intelligence and applications ;$vv. 148
225 1 $aDissertations in artificial intelligence
300   $aDescription based upon print version of record.
311   $a1-58603-674-2 
320   $aIncludes bibliographical references (p. 201-221) and index.
327   $aTitle page; Contents; Abstract; Overture; Part I: Probabilistic ILP over Interpretations; Part II: Probabilistic ILP over Time; Intermezzo: Exploiting Probabilistic ILP in Discriminative Classifiers; Part III: Making Complex Decisions in Relational Domains; Finale; Appendix; Bibliography; Symbol Index; Index
330   $aTalks about Logic Programming, Uncertainty Reasoning and Machine Learning. This book includes definitions that circumscribe the area formed by extending Inductive Logic Programming to cases annotated with probability values. It investigates the approach of Learning from proofs and the issue of upgrading Fisher Kernels to Relational Fisher Kernels.
410  0$aFrontiers in artificial intelligence and applications.$pDissertations in artificial intelligence.
410  0$aFrontiers in artificial intelligence and applications ;$vv. 148.
606   $aLogic programming
606   $aUncertainty (Information theory)
606   $aMachine learning
606   $aMarkov processes
615  0$aLogic programming.
615  0$aUncertainty (Information theory)
615  0$aMachine learning.
615  0$aMarkov processes.
700   $aKersting$b Kristian$01539425
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910784254203321
996   $aAn inductive logic programming approach to statistical relational learning$93790310
997   $aUNINA