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008 | 100701s2010 gw | s |||| 0|eng d | ||
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_a9783642138409 _9978-3-642-13840-9 |
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050 | 4 | _aQA8.9-QA10.3 | |
082 | 0 | 4 |
_a005.131 _223 |
100 | 1 |
_aRaedt, Luc. _eeditor. |
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245 | 1 | 0 |
_aInductive Logic Programming _h[recurso electrónico] : _b19th International Conference, ILP 2009, Leuven, Belgium, July 02-04, 2009. Revised Papers / _cedited by Luc Raedt. |
264 | 1 |
_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg, _c2010. |
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300 |
_aXII, 257p. _bonline resource. |
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_atext _btxt _2rdacontent |
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_acomputer _bc _2rdamedia |
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_aonline resource _bcr _2rdacarrier |
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_atext file _bPDF _2rda |
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_aLecture Notes in Computer Science, _x0302-9743 ; _v5989 |
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505 | 0 | _aKnowledge-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?. | |
520 | _aThe ILP conference series has been the premier forum for work on logic-based approaches to machine learning for almost two decades. The 19th International Conference on Inductive Logic Programming, which was organized in Leuven, July2-4,2009,continuedthistraditionbutalsoreachedouttoothercommunities as it was colocated with SRL-2009 – the International Workshop on Statistical RelationalLearning,andMLG-2009–the7thInternationalWorkshoponMining andLearningwithGraphs. While thesethreeseriesofeventseachhavetheirown focus,emphasis andtradition,they essentiallysharethe problemthatis studied: learning about structured data in the form of graphs, relational descriptions or logic. The colocation of the events was intended to increase the interaction between the three communities. There was a single program with joint invited and tutorial speakers, a panel, regular talks and poster sessions. The invited speakers and tutorial speakers were James Cussens, Jason Eisner, Jure Leskovec, Raymond Mooney, Scott Sanner, and Philip Yu. The panel featured Karsten Borgwardt, Luc De Raedt, Pedro Domingos, Paolo Frasconi, Thomas Gart ¨ ner, Kristian Kersting, Stephen Muggleton, and C. David Page. Video-recordings of these talks can be found atwww. videolectures. net. The overall program featured 30 talks presented in two parallel tracks and 53 posters. The talks and posters were selected on the basis of an extended abstract. These abstracts can be found at http:// dtai. cs. kuleuven. be/ilp-mlg-srl/. Inaddition,asinpreviousyears,a- lectionofthepapersofILP2009havebeenpublishedinavolumeintheLectures Notes in Arti?cial Intelligence seriesandinaspecialissueoftheMachine Lea- ing Journal. | ||
650 | 0 | _aComputer science. | |
650 | 0 | _aComputer software. | |
650 | 0 | _aLogic design. | |
650 | 0 | _aDatabase management. | |
650 | 0 | _aData mining. | |
650 | 0 | _aInformation storage and retrieval systems. | |
650 | 1 | 4 | _aComputer Science. |
650 | 2 | 4 | _aMathematical Logic and Formal Languages. |
650 | 2 | 4 | _aDatabase Management. |
650 | 2 | 4 | _aInformation Storage and Retrieval. |
650 | 2 | 4 | _aAlgorithm Analysis and Problem Complexity. |
650 | 2 | 4 | _aData Mining and Knowledge Discovery. |
650 | 2 | 4 | _aLogics and Meanings of Programs. |
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer eBooks | |
776 | 0 | 8 |
_iPrinted edition: _z9783642138393 |
830 | 0 |
_aLecture Notes in Computer Science, _x0302-9743 ; _v5989 |
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856 | 4 | 0 |
_zLibro electrónico _uhttp://148.231.10.114:2048/login?url=http://link.springer.com/book/10.1007/978-3-642-13840-9 |
596 | _a19 | ||
942 | _cLIBRO_ELEC | ||
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_c202481 _d202481 |