LEADER 04743nam 22008295 450 001 9910484759403321 005 20200703012854.0 010 $a3-319-23708-X 024 7 $a10.1007/978-3-319-23708-4 035 $a(CKB)4340000000001093 035 $a(SSID)ssj0001599540 035 $a(PQKBManifestationID)16305812 035 $a(PQKBTitleCode)TC0001599540 035 $a(PQKBWorkID)14892190 035 $a(PQKB)10782290 035 $a(DE-He213)978-3-319-23708-4 035 $a(MiAaPQ)EBC6296556 035 $a(MiAaPQ)EBC5596052 035 $a(Au-PeEL)EBL5596052 035 $a(OCoLC)933623782 035 $a(PPN)190884568 035 $a(EXLCZ)994340000000001093 100 $a20151226d2015 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aInductive Logic Programming $e24th International Conference, ILP 2014, Nancy, France, September 14-16, 2014, Revised Selected Papers /$fedited by Jesse Davis, Jan Ramon 205 $a1st ed. 2015. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2015. 215 $a1 online resource (X, 211 p. 62 illus. in color.) 225 1 $aLecture Notes in Artificial Intelligence ;$v9046 300 $aIncludes Index. 311 $a3-319-23707-1 327 $aReframing on Relational Data -- Inductive Learning using Constraint-driven Bias -- Nonmonotonic Learning in Large Biological Networks -- Construction of Complex Aggregates with Random Restart Hill-Climbing -- Logical minimisation of meta-rules within Meta-Interpretive Learning -- Goal and plan recognition via parse trees using prefix and infix probability computation -- Effectively creating weakly labeled training examples via approximate domain knowledge -- Learning Prime Implicant Conditions From Interpretation Transition -- Statistical Relational Learning for Handwriting Recognition -- The Most Probable Explanation for Probabilistic Logic Programs with Annotated Disjunctions -- Towards machine learning of predictive models from ecological data -- PageRank, ProPPR, and Stochastic Logic Programs -- Complex aggregates over clusters of elements -- On the Complexity of Frequent Subtree Mining in Very Simple Structures. 330 $aThis book constitutes the thoroughly refereed post-conference proceedings of the 24th International Conference on Inductive Logic Programming, ILP 2014, held in Nancy, France, in September 2014. The 14 revised papers presented were carefully reviewed and selected from 41 submissions. The papers focus on topics such as the inducing of logic programs, learning from data represented with logic, multi-relational machine learning, learning from graphs, and applications of these techniques to important problems in fields like bioinformatics, medicine, and text mining. 410 0$aLecture Notes in Artificial Intelligence ;$v9046 606 $aMathematical logic 606 $aArtificial intelligence 606 $aComputer programming 606 $aApplication software 606 $aComputer logic 606 $aComputers 606 $aMathematical Logic and Formal Languages$3https://scigraph.springernature.com/ontologies/product-market-codes/I16048 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 $aInformation Systems Applications (incl. Internet)$3https://scigraph.springernature.com/ontologies/product-market-codes/I18040 606 $aLogics and Meanings of Programs$3https://scigraph.springernature.com/ontologies/product-market-codes/I1603X 606 $aComputation by Abstract Devices$3https://scigraph.springernature.com/ontologies/product-market-codes/I16013 615 0$aMathematical logic. 615 0$aArtificial intelligence. 615 0$aComputer programming. 615 0$aApplication software. 615 0$aComputer logic. 615 0$aComputers. 615 14$aMathematical Logic and Formal Languages. 615 24$aArtificial Intelligence. 615 24$aProgramming Techniques. 615 24$aInformation Systems Applications (incl. Internet). 615 24$aLogics and Meanings of Programs. 615 24$aComputation by Abstract Devices. 676 $a005.115 702 $aDavis$b Jesse$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aRamon$b Jan$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910484759403321 996 $aInductive Logic Programming$92804417 997 $aUNINA