Inductive Logic Programming [electronic resource] : 20th International Conference, ILP 2010, Florence, Italy, June 27-30, 2010. Revised Papers / edited by Paolo Frasconi, Francesca A. Lisi.
By: Frasconi, Paolo [editor.].
Contributor(s): Lisi, Francesca A [editor.] | SpringerLink (Online service).
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BookSeries: Lecture Notes in Computer Science: 6489Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2011Description: XI, 278p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783642212956.Subject(s): Computer science | Database management | Information storage and retrieval systems | Artificial intelligence | Computer Science | Artificial Intelligence (incl. Robotics) | Mathematical Logic and Formal Languages | Computation by Abstract Devices | Information Storage and Retrieval | Database ManagementDDC classification: 006.3 Online resources: Click here to access online
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Springer eBooksSummary: This book constitutes the thoroughly refereed post-proceedings of the 20th International Conference on Inductive Logic Programming, ILP 2010, held in Florence, Italy in June 2010. The 11 revised full papers and 15 revised short papers presented together with abstracts of three invited talks were carefully reviewed and selected during two rounds of refereeing and revision. All current issues in inductive logic programming, i.e. in logic programming for machine learning are addressed, in particular statistical learning and other probabilistic approaches to machine learning are reflected.
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This book constitutes the thoroughly refereed post-proceedings of the 20th International Conference on Inductive Logic Programming, ILP 2010, held in Florence, Italy in June 2010. The 11 revised full papers and 15 revised short papers presented together with abstracts of three invited talks were carefully reviewed and selected during two rounds of refereeing and revision. All current issues in inductive logic programming, i.e. in logic programming for machine learning are addressed, in particular statistical learning and other probabilistic approaches to machine learning are reflected.
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