Inductive Logic Programming: 17th International Conference, ILP 2007, Corvallis, OR, USA, June 19-21, 2007, Revised Selected Papers

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Hendrik Blockeel, Jude Shavlik
Springer Science & Business Media, Mar 14, 2008 - Computers - 307 pages
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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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Contents

Learning with Kernels and Logical Representations
1
Directions for Probabilistic and Relational Learning
4
Learning Probabilistic Logic Models from Probabilistic Examples Extended Abstract
22
OrderingSearch
24
Learning to Assign Degrees of Belief in Relational Domains
25
BiasVariance Analysis for Relational Domains
27
Induction of Optimal Semantic Semidistances for Clausal Knowledge Bases
29
Clustering Relational Data Based on Randomized Propositionalization
39
Applying Inductive Logic Programming to Process Mining
132
A Refinement Operator Based Learning Algorithm for the ALC Description Logic
147
Foundations of Refinement Operators for Description Logics
161
A Relational Hierarchical Model for DecisionTheoretic Assistance
175
Using Bayesian Networks to Direct Stochastic Search in Inductive Logic Programming
191
Revising FirstOrder Logic Theories from Examples Through Stochastic Local Search
200
Using ILP to Construct Features for Information Extraction from Semistructured Text
211
ModeDirected Inverse Entailment for Full Clausal Theories
225

Structural Statistical Software Testing with Active Learning in a Graph
49
Learning Declarative Bias
63
Just Trie It
78
Learning Relational Options for Inductive Transfer in Relational Reinforcement Learning
88
Empirical Comparison of Hard and Soft Label Propagation for Relational Classification
98
A Phase TransitionBased Perspective on Multiple Instance Kernels
112
with Various Precisions and Recalls to Produce Accurate Probabilistic Estimates
122
Mining of Frequent Block Preserving Outerplanar Graph Structured Patterns
239
Relational Macros for Transfer in Reinforcement Learning
254
Learning a Comprehensible Model from a First Order Ensemble
269
Building Relational World Models for Reinforcement Learning
280
An Inductive Learning System for XML Documents
292
Author Index
307
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