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Record Nr. |
UNINA9910768183503321 |
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Titolo |
Inductive Logic Programming : 13th International Conference, ILP 2003, Szeged, Hungary, September 29 - October 1, 2003, Proceedings / / edited by Tamas Horváth, Akihiro Yamamoto |
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Pubbl/distr/stampa |
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Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2003 |
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ISBN |
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Edizione |
[1st ed. 2003.] |
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Descrizione fisica |
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1 online resource (X, 406 p.) |
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Collana |
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Lecture Notes in Artificial Intelligence ; ; 2835 |
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Disciplina |
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Soggetti |
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Software engineering |
Artificial intelligence |
Computer science |
Computer programming |
Logic, Symbolic and mathematical |
Software Engineering/Programming and Operating Systems |
Artificial Intelligence |
Computer Science, general |
Programming Techniques |
Mathematical Logic and Formal Languages |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Bibliographic Level Mode of Issuance: Monograph |
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Nota di bibliografia |
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Includes bibliographical references at the end of each chapters and index. |
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Nota di contenuto |
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Invited Papers -- A Personal View of How Best to Apply ILP -- Agents that Reason and Learn -- Research Papers -- Mining Model Trees: A Multi-relational Approach -- Complexity Parameters for First-Order Classes -- A Multi-relational Decision Tree Learning Algorithm – Implementation and Experiments -- Applying Theory Revision to the Design of Distributed Databases -- Disjunctive Learning with a Soft-Clustering Method -- ILP for Mathematical Discovery -- An Exhaustive Matching Procedure for the Improvement of Learning Efficiency -- Efficient Data Structures for Inductive Logic Programming -- Graph Kernels and Gaussian Processes for Relational Reinforcement Learning -- On Condensation of a Clause -- A Comparative Evaluation of |
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Feature Set Evolution Strategies for Multirelational Boosting -- Comparative Evaluation of Approaches to Propositionalization -- Ideal Refinement of Descriptions in -Log -- Which First-Order Logic Clauses Can Be Learned Using Genetic Algorithms? -- Improved Distances for Structured Data -- Induction of Enzyme Classes from Biological Databases -- Estimating Maximum Likelihood Parameters for Stochastic Context-Free Graph Grammars -- Induction of the Effects of Actions by Monotonic Methods -- Hybrid Abductive Inductive Learning: A Generalisation of Progol -- Query Optimization in Inductive Logic Programming by Reordering Literals -- Efficient Learning of Unlabeled Term Trees with Contractible Variables from Positive Data -- Relational IBL in Music with a New Structural Similarity Measure -- An Effective Grammar-Based Compression Algorithm for Tree Structured Data. |
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Sommario/riassunto |
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The13thInternationalConferenceonInductive LogicProgramming(ILP 2003), organizedbytheDepartmentofInformaticsattheUniversityofSzeged,washeld between September 29 and October 1, 2003 in Szeged, Hungary. ILP 2003 was co-located with the Kalm´ ar Workshop on Logic and Computer Science devoted to the workofL´ aszl´oKalm´ arandto recentresultsinlogicandcomputerscience. This volume contains all full papers presented at ILP 2003, together with the abstracts of the invited lectures by Ross D. King (University of Wales, Aber- twyth) and John W. Lloyd (Australian National University, Canberra). TheILP conferenceseries,startedin1991,wasoriginallydesignedto provide an international forum for the presentation and discussion of the latest research resultsinallareasoflearninglogicprograms.InrecentyearsthescopeofILPhas been broadened to cover theoretical, algorithmic, empirical, and applicational aspects of learning in non-propositional logic, multi-relational learning and data mining, and learning from structured and semi-structured data. The program committee received altogether 58 submissions in response to the call for papers, of which 5 were withdrawn by the authors themselves. Out of the remaining 53 submissions, the program committee selected 23 papers for full presentation at ILP 2003. High reviewing standards were applied for the selection of the papers. For the ?rst time, the "Machine Learning" journal awarded the best student papers. The awards were presented to Marta Arias for her theoretical paper withRoniKhardon:ComplexityParametersforFirst-OrderClasses,andtoKurt DriessensandThomasG¨ artnerfortheirjointalgorithmicpaperwithJanRamon: Graph Kernels and Gaussian Processes for Relational Reinforcement Learning. |
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