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, Vítor Santos Costa, Aline Paes.
Contributor(s): Zaverucha, Gerson [editor.]
| Santos Costa, Vítor [editor.] | Paes, Aline [editor.] | SpringerLink (Online service).
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BookSeries: Lecture Notes in Artificial Intelligence: 8812Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2014Edition: 1st ed. 2014.Description: XIII, 141 p. 31 illus. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783662449233.Subject(s): Machine theory | Artificial intelligence | Computer programming | Computer science | Application software | Formal Languages and Automata Theory | Artificial Intelligence | Programming Techniques | Computer Science Logic and Foundations of Programming | Theory of Computation | Computer and Information Systems ApplicationsAdditional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification: 005.131 Online resources: Click here to access online
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Springer Nature eBookSummary: 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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