LEADER 04425nam 22007695 450 001 996465810803316 005 20200706072216.0 010 $a3-540-44797-0 024 7 $a10.1007/3-540-44797-0 035 $a(CKB)1000000000211555 035 $a(SSID)ssj0000323940 035 $a(PQKBManifestationID)11285439 035 $a(PQKBTitleCode)TC0000323940 035 $a(PQKBWorkID)10303836 035 $a(PQKB)11534178 035 $a(DE-He213)978-3-540-44797-9 035 $a(MiAaPQ)EBC3072315 035 $a(PPN)155210513 035 $a(EXLCZ)991000000000211555 100 $a20121227d2001 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aInductive Logic Programming$b[electronic resource] $e11th International Conference, ILP 2001, Strasbourg, France, September 9-11, 2001. Proceedings /$fedited by Celine Rouveirol, Michele Sebag 205 $a1st ed. 2001. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2001. 215 $a1 online resource (IX, 259 p.) 225 1 $aLecture Notes in Artificial Intelligence ;$v2157 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-540-42538-1 320 $aIncludes bibliographical references and index. 327 $aA Refinement Operator for Theories -- Learning Logic Programs with Neural Networks -- A Genetic Algorithm for Propositionalization -- Classifying Uncovered Examples by Rule Stretching -- Relational Learning Using Constrained Confidence-Rated Boosting -- Induction, Abduction, and Consequence-Finding -- From Shell Logs to Shell Scripts -- An Automated ILP Server in the Field of Bioinformatics -- Adaptive Bayesian Logic Programs -- Towards Combining Inductive Logic Programming with Bayesian Networks -- Demand-Driven Construction of Structural Features in ILP -- Transformation-Based Learning Using Multirelational Aggregation -- Discovering Associations between Spatial Objects: An ILP Application -- ?-Subsumption in a Constraint Satisfaction Perspective -- Learning to Parse from a Treebank: Combining TBL and ILP -- Induction of Stable Models -- Application of Pruning Techniques for Propositional Learning to Progol -- Application of ILP to Cardiac Arrhythmia Characterization for Chronicle Recognition -- Efficient Cross-Validation in ILP -- Modelling Semi-structured Documents with Hedges for Deduction and Induction -- Learning Functions from Imperfect Positive Data. 410 0$aLecture Notes in Artificial Intelligence ;$v2157 606 $aArchitecture, Computer 606 $aSoftware engineering 606 $aArtificial intelligence 606 $aComputer programming 606 $aMathematical logic 606 $aAlgorithms 606 $aComputer System Implementation$3https://scigraph.springernature.com/ontologies/product-market-codes/I13057 606 $aSoftware Engineering/Programming and Operating Systems$3https://scigraph.springernature.com/ontologies/product-market-codes/I14002 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aProgramming Techniques$3https://scigraph.springernature.com/ontologies/product-market-codes/I14010 606 $aMathematical Logic and Formal Languages$3https://scigraph.springernature.com/ontologies/product-market-codes/I16048 606 $aAlgorithm Analysis and Problem Complexity$3https://scigraph.springernature.com/ontologies/product-market-codes/I16021 615 0$aArchitecture, Computer. 615 0$aSoftware engineering. 615 0$aArtificial intelligence. 615 0$aComputer programming. 615 0$aMathematical logic. 615 0$aAlgorithms. 615 14$aComputer System Implementation. 615 24$aSoftware Engineering/Programming and Operating Systems. 615 24$aArtificial Intelligence. 615 24$aProgramming Techniques. 615 24$aMathematical Logic and Formal Languages. 615 24$aAlgorithm Analysis and Problem Complexity. 676 $a005.1/15 702 $aRouveirol$b Celine$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aSebag$b Michele$4edt$4http://id.loc.gov/vocabulary/relators/edt 712 12$aILP (Conference) 906 $aBOOK 912 $a996465810803316 996 $aInductive Logic Programming$9772244 997 $aUNISA