Inductive Logic Programming [electronic resource] : 23rd International Conference, ILP 2013, Rio de Janeiro, Brazil, August 28-30, 2013, Revised Selected Papers / edited by Gerson Zaverucha, V�itor Santos Costa, Aline Paes.
Contributor(s): Zaverucha, Gerson [editor.] | Santos Costa, V�itor [editor.] | Paes, Aline [editor.] | SpringerLink (Online service).
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BookSeries: Lecture Notes in Computer Science: 8812Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2014Description: XIII, 141 p. 31 illus. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783662449233.Subject(s): Computer science | Computer programming | Computers | Computer logic | Mathematical logic | Artificial intelligence | Computer Science | Mathematical Logic and Formal Languages | Artificial Intelligence (incl. Robotics) | Programming Techniques | Logics and Meanings of Programs | Computation by Abstract Devices | Information Systems Applications (incl. Internet)Additional physical formats: Printed edition:: No titleDDC classification: 005.131 Online resources: Click here to access online
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Springer eBooksSummary: This book constitutes the thoroughly refereed post-proceedings of the 23rd International Conference on Inductive Logic Programming, ILP 2013, held in Rio de Janeiro, Brazil, in August 2013. The 9 revised extended papers were carefully reviewed and selected from 42 submissions. The conference now focuses on all aspects of learning in logic, multi-relational learning and data mining, statistical relational learning, graph and tree mining, relational reinforcement learning, and other forms of learning from structured data.
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This book constitutes the thoroughly refereed post-proceedings of the 23rd International Conference on Inductive Logic Programming, ILP 2013, held in Rio de Janeiro, Brazil, in August 2013. The 9 revised extended papers were carefully reviewed and selected from 42 submissions. The conference now focuses on all aspects of learning in logic, multi-relational learning and data mining, statistical relational learning, graph and tree mining, relational reinforcement learning, and other forms of learning from structured data.
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