Buch, Englisch, 257 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 413 g
19th International Conference, ILP 2009, Leuven, Belgium, July 2-4, 2010, Revised Papers
Buch, Englisch, 257 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 413 g
Reihe: Lecture Notes in Artificial Intelligence
ISBN: 978-3-642-13839-3
Verlag: Springer
The 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.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Warehouse
- Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining
- Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Algorithmen & Datenstrukturen
- Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Programmier- und Skriptsprachen
Weitere Infos & Material
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?.
