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.
Contributor(s): Frasconi, Paolo [editor.]
| Lisi, Francesca A [editor.] | SpringerLink (Online service).
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BookSeries: Lecture Notes in Artificial Intelligence: 6489Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2011Edition: 1st ed. 2011.Description: XI, 278 p. 67 illus., 29 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783642212956.Subject(s): Artificial intelligence | Machine theory | Computer science | Information storage and retrieval systems | Database management | Artificial Intelligence | Formal Languages and Automata Theory | Theory of Computation | Information Storage and Retrieval | Database ManagementAdditional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification: 006.3 Online resources: Click here to access online
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Springer Nature eBookSummary: 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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