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|a Inductive Logic Programming
|h [electronic resource] :
|b 19th International Conference, ILP 2009, Leuven, Belgium, July 2-4, 2010, Revised Papers /
|c edited by Luc Raedt.
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|a 1st ed. 2010.
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|a Berlin, Heidelberg :
|b Springer Berlin Heidelberg :
|b Imprint: Springer,
|c 2010.
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|a XII, 257 p.
|b online resource.
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|a text
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|a Lecture Notes in Artificial Intelligence,
|x 2945-9141 ;
|v 5989
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|a Knowledge-Directed Theory Revision -- Towards Clausal Discovery for Stream Mining -- On the Relationship between Logical Bayesian Networks and Probabilistic Logic Programming Based on the Distribution Semantics -- Induction of Relational Algebra Expressions -- A Logic-Based Approach to Relation Extraction from Texts -- Discovering Rules by Meta-level Abduction -- Inductive Generalization of Analytically Learned Goal Hierarchies -- Ideal Downward Refinement in the Description Logic -- Nonmonotonic Onto-Relational Learning -- CP-Logic Theory Inference with Contextual Variable Elimination and Comparison to BDD Based Inference Methods -- Speeding Up Inference in Statistical Relational Learning by Clustering Similar Query Literals -- Chess Revision: Acquiring the Rules of Chess Variants through FOL Theory Revision from Examples -- ProGolem: A System Based on Relative Minimal Generalisation -- An Inductive Logic Programming Approach to Validate Hexose Binding Biochemical Knowledge -- Boosting First-Order Clauses for Large, Skewed Data Sets -- Incorporating Linguistic Expertise Using ILP for Named Entity Recognition in Data Hungry Indian Languages -- Transfer Learning via Relational Templates -- Automatic Revision of Metabolic Networks through Logical Analysis of Experimental Data -- Finding Relational Associations in HIV Resistance Mutation Data -- ILP, the Blind, and the Elephant: Euclidean Embedding of Co-proven Queries -- Parameter Screening and Optimisation for ILP Using Designed Experiments -- Don't Fear Optimality: Sampling for Probabilistic-Logic Sequence Models -- Policy Transfer via Markov Logic Networks -- Can ILP Be Applied to Large Datasets?.
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|a Machine theory.
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|a Compilers (Computer programs).
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|a Database management.
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|a Information storage and retrieval systems.
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|a Algorithms.
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|a Data mining.
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|a Formal Languages and Automata Theory.
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|a Compilers and Interpreters.
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| 650 |
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|a Database Management.
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| 650 |
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4 |
|a Information Storage and Retrieval.
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| 650 |
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|a Algorithms.
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| 650 |
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|a Data Mining and Knowledge Discovery.
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| 700 |
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|a Raedt, Luc.
|e editor.
|4 edt
|4 http://id.loc.gov/vocabulary/relators/edt
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| 710 |
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|a SpringerLink (Online service)
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|t Springer Nature eBook
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|i Printed edition:
|z 9783642138393
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|i Printed edition:
|z 9783642138416
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|a Lecture Notes in Artificial Intelligence,
|x 2945-9141 ;
|v 5989
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|u https://doi.uam.elogim.com/10.1007/978-3-642-13840-9
|z Texto Completo
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