LEADER 06257nam 22008175 450 001 9910143883103321 005 20200706010232.0 010 $a3-540-36468-4 024 7 $a10.1007/3-540-36468-4 035 $a(CKB)1000000000211917 035 $a(SSID)ssj0000323941 035 $a(PQKBManifestationID)11912656 035 $a(PQKBTitleCode)TC0000323941 035 $a(PQKBWorkID)10304165 035 $a(PQKB)10660474 035 $a(DE-He213)978-3-540-36468-9 035 $a(MiAaPQ)EBC3072455 035 $a(PPN)155207547 035 $a(EXLCZ)991000000000211917 100 $a20121227d2003 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aInductive Logic Programming $e12th International Conference, ILP 2002, Sydney, Australia, July 9-11, 2002. Revised Papers /$fedited by Stan Matwin, Claude Sammut 205 $a1st ed. 2003. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2003. 215 $a1 online resource (X, 358 p.) 225 1 $aLecture Notes in Artificial Intelligence ;$v2583 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-540-00567-6 320 $aIncludes bibliographical references and index. 327 $aContributed Papers -- Propositionalization for Clustering Symbolic Relational Descriptions -- Efficient and Effective Induction of First Order Decision Lists -- Learning with Feature Description Logics -- An Empirical Evaluation of Bagging in Inductive Logic Programming -- Kernels for Structured Data -- Experimental Comparison of Graph-Based Relational Concept Learning with Inductive Logic Programming Systems -- Autocorrelation and Linkage Cause Bias in Evaluation of Relational Learners -- Learnability of Description Logic Programs -- 1BC2: A True First-Order Bayesian Classifier -- RSD: Relational Subgroup Discovery through First-Order Feature Construction -- Mining Frequent Logical Sequences with SPIRIT-LoG -- Using Theory Completion to Learn a Robot Navigation Control Program -- Learning Structure and Parameters of Stochastic Logic Programs -- A Novel Approach to Machine Discovery: Genetic Programming and Stochastic Grammars -- Revision of First-Order Bayesian Classifiers -- The Applicability to ILP of Results Concerning the Ordering of Binomial Populations -- Compact Representation of Knowledge Bases in ILP -- A Polynomial Time Matching Algorithm of Structured Ordered Tree Patterns for Data Mining from Semistructured Data -- A Genetic Algorithms Approach to ILP -- Experimental Investigation of Pruning Methods for Relational Pattern Discovery -- Noise-Resistant Incremental Relational Learning Using Possible Worlds -- Lattice-Search Runtime Distributions May Be Heavy-Tailed -- Invited Talk Abstracts -- Learning in Rich Representations: Inductive Logic Programming and Computational Scientific Discovery. 330 $aThe Twelfth International Conference on Inductive Logic Programming was held in Sydney, Australia, July 9?11, 2002. The conference was colocated with two other events, the Nineteenth International Conference on Machine Learning (ICML2002) and the Fifteenth Annual Conference on Computational Learning Theory (COLT2002). Startedin1991,InductiveLogicProgrammingistheleadingannualforumfor researchers working in Inductive Logic Programming and Relational Learning. Continuing a series of international conferences devoted to Inductive Logic Programming and Relational Learning, ILP 2002 was the central event in 2002 for researchers interested in learning relational knowledge from examples. The Program Committee, following a resolution of the Community Me- ing in Strasbourg in September 2001, took upon itself the issue of the possible change of the name of the conference. Following an extended e-mail discussion, a number of proposed names were subjected to a vote. In the ?rst stage of the vote, two names were retained for the second vote. The two names were: Ind- tive Logic Programming, and Relational Learning. It had been decided that a 60% vote would be needed to change the name; the result of the vote was 57% in favor of the name Relational Learning. Consequently, the name Inductive Logic Programming was kept. 410 0$aLecture Notes in Artificial Intelligence ;$v2583 606 $aSoftware engineering 606 $aArtificial intelligence 606 $aComputer science 606 $aComputer programming 606 $aAlgorithms 606 $aLogic, Symbolic and mathematical 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 $aComputer Science, general$3https://scigraph.springernature.com/ontologies/product-market-codes/I00001 606 $aProgramming Techniques$3https://scigraph.springernature.com/ontologies/product-market-codes/I14010 606 $aAlgorithm Analysis and Problem Complexity$3https://scigraph.springernature.com/ontologies/product-market-codes/I16021 606 $aMathematical Logic and Formal Languages$3https://scigraph.springernature.com/ontologies/product-market-codes/I16048 615 0$aSoftware engineering. 615 0$aArtificial intelligence. 615 0$aComputer science. 615 0$aComputer programming. 615 0$aAlgorithms. 615 0$aLogic, Symbolic and mathematical. 615 14$aSoftware Engineering/Programming and Operating Systems. 615 24$aArtificial Intelligence. 615 24$aComputer Science, general. 615 24$aProgramming Techniques. 615 24$aAlgorithm Analysis and Problem Complexity. 615 24$aMathematical Logic and Formal Languages. 676 $a005.1/15 702 $aMatwin$b Stan$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aSammut$b Claude$4edt$4http://id.loc.gov/vocabulary/relators/edt 712 12$aILP (Conference) 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910143883103321 996 $aInductive Logic Programming$92804417 997 $aUNINA