Inductive Logic Programming

17th International Conference, ILP 2007, Corvallis, OR, USA, June 19-21, 2007, Revised Selected Papers
Herausgegeben:Blockeel, Hendrik; Ramon, Jan; Shavlik, Jude; Tadepalli, Prasad

• Broschiertes Buch

Produktbeschreibung

ILP 2007, the 17th Conference on Inductive Logic Programming, was held in Corvallis, Oregon, USA, June 19 21, and was collocated with the 24th Inter- tional Conferenceon Machine Learning.The programconsisted of 15 full and 14 short presentations, a poster session, keynote talks by Paolo Frasconi (Learning withKernelsandLogicalRepresentations)andDavidJensen(BeyondPrediction: Directions for Probabilistic and Relational Learning), and several joint sessions with ICML. Thirty-eight submissions were received this year, out of which ?fteen were accepted for publication in the proceedings as full papers and eleven as short papers.Inclusionin the proceedings was decided bytaking into accountnotonly the relevance and quality of the work described, but also the quality and level of maturityofthetext.Severalmoresubmissionswereacceptedaswork-in-progress presentations. Thus the 2007 edition of ILP continued the tradition of adopting high selectivity for published papers, while at the same time o?ering a forum for work in progress. All accepted papers were made available in temporary online proceedings during the conference. Revised versions of the submitted papers, incorporating feedback from discussions at the conference, are included either in the proce- ings of the conference (this volume) or, for a small number of selected papers, in a special issue of theMachine Learning journal (abstracts of these are included in this volume). Papers reporting on work in progress remain available in the online proceedings, athttp://pages.cs.wisc.edu/~shavlik/ilp07wip/.

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Produktdetails

• Lecture Notes in Computer Science 4894
• Verlag: Springer / Springer Berlin Heidelberg / Springer, Berlin
• Artikelnr. des Verlages: 12236039, 978-3-540-78468-5
• 2008
• Seitenzahl: 328
• Erscheinungstermin: 14. März 2008
• Englisch
• Abmessung: 235mm x 155mm x 18mm
• Gewicht: 504g
• ISBN-13: 9783540784685
• ISBN-10: 3540784683
• Artikelnr.: 23533580

Inhaltsangabe

Invited Talks.- Learning with Kernels and Logical Representations.- Beyond Prediction: Directions for Probabilistic and Relational Learning.- Extended Abstracts.- Learning Probabilistic Logic Models from Probabilistic Examples (Extended Abstract).- Learning Directed Probabilistic Logical Models Using Ordering-Search.- Learning to Assign Degrees of Belief in Relational Domains.- Bias/Variance Analysis for Relational Domains.- Full Papers.- Induction of Optimal Semantic Semi-distances for Clausal Knowledge Bases.- Clustering Relational Data Based on Randomized Propositionalization.- Structural Statistical Software Testing with Active Learning in a Graph.- Learning Declarative Bias.- ILP :- Just Trie It.- Learning Relational Options for Inductive Transfer in Relational Reinforcement Learning.- Empirical Comparison of "Hard" and "Soft" Label Propagation for Relational Classification.- A Phase Transition-Based Perspective on Multiple Instance Kernels.- Combining Clauses with Various Precisions and Recalls to Produce Accurate Probabilistic Estimates.- Applying Inductive Logic Programming to Process Mining.- A Refinement Operator Based Learning Algorithm for the Description Logic.- Foundations of Refinement Operators for Description Logics.- A Relational Hierarchical Model for Decision-Theoretic Assistance.- Using Bayesian Networks to Direct Stochastic Search in Inductive Logic Programming.- Revising First-Order Logic Theories from Examples Through Stochastic Local Search.- Using ILP to Construct Features for Information Extraction from Semi-structured Text.- Mode-Directed Inverse Entailment for Full Clausal Theories.- Mining of Frequent Block Preserving Outerplanar Graph Structured Patterns.- Relational Macros for Transfer in Reinforcement Learning.- Seeing theForest Through the Trees.- Building Relational World Models for Reinforcement Learning.- An Inductive Learning System for XML Documents.