04768nam 22008295 450 99646645940331620200703012854.03-319-23708-X10.1007/978-3-319-23708-4(CKB)4340000000001093(SSID)ssj0001599540(PQKBManifestationID)16305812(PQKBTitleCode)TC0001599540(PQKBWorkID)14892190(PQKB)10782290(DE-He213)978-3-319-23708-4(MiAaPQ)EBC6296556(MiAaPQ)EBC5596052(Au-PeEL)EBL5596052(OCoLC)933623782(PPN)190884568(EXLCZ)99434000000000109320151226d2015 u| 0engurnn|008mamaatxtccrInductive Logic Programming[electronic resource] 24th International Conference, ILP 2014, Nancy, France, September 14-16, 2014, Revised Selected Papers /edited by Jesse Davis, Jan Ramon1st ed. 2015.Cham :Springer International Publishing :Imprint: Springer,2015.1 online resource (X, 211 p. 62 illus. in color.) Lecture Notes in Artificial Intelligence ;9046Includes Index.3-319-23707-1 Reframing 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.This 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.Lecture Notes in Artificial Intelligence ;9046Mathematical logicArtificial intelligenceComputer programmingApplication softwareComputer logicComputersMathematical Logic and Formal Languageshttps://scigraph.springernature.com/ontologies/product-market-codes/I16048Artificial Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/I21000Programming Techniqueshttps://scigraph.springernature.com/ontologies/product-market-codes/I14010Information Systems Applications (incl. Internet)https://scigraph.springernature.com/ontologies/product-market-codes/I18040Logics and Meanings of Programshttps://scigraph.springernature.com/ontologies/product-market-codes/I1603XComputation by Abstract Deviceshttps://scigraph.springernature.com/ontologies/product-market-codes/I16013Mathematical logic.Artificial intelligence.Computer programming.Application software.Computer logic.Computers.Mathematical Logic and Formal Languages.Artificial Intelligence.Programming Techniques.Information Systems Applications (incl. Internet).Logics and Meanings of Programs.Computation by Abstract Devices.005.115Davis Jesseedthttp://id.loc.gov/vocabulary/relators/edtRamon Janedthttp://id.loc.gov/vocabulary/relators/edtMiAaPQMiAaPQMiAaPQBOOK996466459403316Inductive Logic Programming2804417UNISA