LEADER 01023nam 2200373 450 001 9910317820803321 005 20221021212840.0 010 $a1-83881-387-X 010 $a1-78923-119-1 035 $a(CKB)4970000000099876 035 $a(NjHacI)994970000000099876 035 $a(EXLCZ)994970000000099876 100 $a20221021d2018 uy 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aUranium $eSafety, Resources, Separation and Thermodynamic Calculation /$fedited by Nasser S. Awwad 210 1$aLondon :$cIntechOpen,$d2018. 210 4$dİ2018 215 $a1 online resource (152 pages) $cillustrations 311 $a1-78923-118-3 517 $aUranium 606 $aUranium 615 0$aUranium. 676 $a546.431 702 $aAwwad$b Nasser S. 801 0$bNjHacI 801 1$bNjHacl 906 $aBOOK 912 $a9910317820803321 996 $aUranium$92954854 997 $aUNINA LEADER 05824nam 22007935 450 001 9910143626803321 005 20250724092017.0 010 $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(BIP)7351624 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 $e11th International Conference, ILP 2001, Strasbourg, France, September 9-11, 2001. Proceedings /$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 $aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v2157 300 $aBibliographic Level Mode of Issuance: Monograph 311 08$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. 330 $aThe 11th international conference on Inductive Logic Programming, ILP2001, was held in Strasbourg, France, September 9-11, 2001. ILP2001 was co-located withthe3rdinternationalworkshoponLogic,Learning,andLanguage(LLL2001), and nearly co-located with the joint 12th European Conference on Machine Learning (ECML2001) and 5th European conference on Principles and Practice of Knowledge Discovery in Databases (PKDD2001). Continuing a series of international conferences devoted to Inductive Logic Programming and Relational Learning, ILP2001 is the central annual event for researchersinterestedinlearningstructuredknowledgefromstructuredexamples and background knowledge. One recent one major challenge for ILP has been to contribute to the ex- nentialemergenceofDataMining,andtoaddressthehandlingofmulti-relational databases. On the one hand, ILP has developed a body of theoretical results and algorithmicstrategiesforexploringrelationaldata,essentiallybutnotexclusively from a supervised learning viewpoint. These results are directly relevant to an e'cient exploration of multi-relational databases. Ontheotherhand,DataMiningmightrequirespeci'crelationalstrategiesto be developed, especially with regard to the scalability issue. The near-colocation of ILP2001 with ECML2001-PKDD2001 was an incentive to increase cro- fertilization between the ILP relational savoir-faire and the new problems and learning goals addressed and to be addressed in Data Mining. Thirty-seven papers were submitted to ILP, among which twenty-one were selected and appear in these proceedings. Several - non-disjoint - trends can be observed, along an admittedly subjective clustering. On the theoretical side, a new mode of inference is proposed by K. Inoue, analog to the open-ended mode of Bayesian reasoning (where the frontier - tween induction and abduction wanes). New learning re'nement operators are proposed by L. Badea, while R. Otero investigates negation-handling settings. 410 0$aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v2157 606 $aComputer systems 606 $aSoftware engineering 606 $aArtificial intelligence 606 $aComputer programming 606 $aMachine theory 606 $aAlgorithms 606 $aComputer System Implementation 606 $aSoftware Engineering 606 $aArtificial Intelligence 606 $aProgramming Techniques 606 $aFormal Languages and Automata Theory 606 $aAlgorithms 615 0$aComputer systems. 615 0$aSoftware engineering. 615 0$aArtificial intelligence. 615 0$aComputer programming. 615 0$aMachine theory. 615 0$aAlgorithms. 615 14$aComputer System Implementation. 615 24$aSoftware Engineering. 615 24$aArtificial Intelligence. 615 24$aProgramming Techniques. 615 24$aFormal Languages and Automata Theory. 615 24$aAlgorithms. 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 $a9910143626803321 996 $aInductive Logic Programming$92804417 997 $aUNINA