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020 _a9783319780900
_9978-3-319-78090-0
024 7 _a10.1007/978-3-319-78090-0
_2doi
050 4 _aQA267-268.5
072 7 _aUYA
_2bicssc
072 7 _aCOM014000
_2bisacsh
072 7 _aUYA
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082 0 4 _a005.131
_223
245 1 0 _aInductive Logic Programming
_h[electronic resource] :
_b27th International Conference, ILP 2017, Orléans, France, September 4-6, 2017, Revised Selected Papers /
_cedited by Nicolas Lachiche, Christel Vrain.
250 _a1st ed. 2018.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2018.
300 _aX, 185 p. 101 illus., 7 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aLecture Notes in Artificial Intelligence,
_x2945-9141 ;
_v10759
505 0 _aRelational Affordance Learning for Task-dependent Robot Grasping -- Positive and Unlabeled Relational Classification Through Label Frequency Estimation -- On Applying Probabilistic Logic Programming to Breast Cancer Data -- Logical Vision: One-Shot Meta-Interpretive Learning from Real Images -- Demystifying Relational Latent Representations -- Parallel Online Learning of Event Definitions -- Relational Restricted Boltzmann Machines: A Probabilistic Logic Learning Approach -- Parallel Inductive Logic Programming System for Super-linear Speedup -- Inductive Learning from State Transitions over Continuous Domains -- Stacked Structure Learning for Lifted Relational Neural Networks -- Pruning Hypothesis Spaces Using Learned Domain Theories -- An Investigation into the Role of Domain-knowledge on the Use of Embeddings.
520 _aThis book constitutes the thoroughly refereed post-conference proceedings of the 27th International Conference on Inductive Logic Programming, ILP 2017, held in Orléans, France, in September 2017. The 12 full papers presented were carefully reviewed and selected from numerous submissions. Inductive Logic Programming (ILP) is a subfield of machine learning, which originally relied on logic programming as a uniform representation language for expressing examples, background knowledge and hypotheses. Due to its strong representation formalism, based on first-order logic, ILP provides an excellent means for multi-relational learning and data mining, and more generally for learning from structured data.
650 0 _aMachine theory.
_9130407
650 0 _aArtificial intelligence.
_93407
650 0 _aCompilers (Computer programs).
_93350
650 0 _aComputer science.
_99832
650 0 _aComputer programming.
_94169
650 1 4 _aFormal Languages and Automata Theory.
_9130408
650 2 4 _aArtificial Intelligence.
_93407
650 2 4 _aCompilers and Interpreters.
_931853
650 2 4 _aComputer Science Logic and Foundations of Programming.
_942203
650 2 4 _aProgramming Techniques.
_9130409
700 1 _aLachiche, Nicolas.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_9130410
700 1 _aVrain, Christel.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_9130411
710 2 _aSpringerLink (Online service)
_9130412
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319780894
776 0 8 _iPrinted edition:
_z9783319780917
830 0 _aLecture Notes in Artificial Intelligence,
_x2945-9141 ;
_v10759
_9130413
856 4 0 _uhttps://doi.org/10.1007/978-3-319-78090-0
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