Inductive Logic Programming

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

Editors:

ISBN: 978-3-540-78468-5 (Print) 978-3-540-78469-2 (Online)

Table of contents (28 chapters)

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  1. Front Matter

    Pages -

  2. Invited Talks

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      Book Chapter

      Pages 1-3

      Learning with Kernels and Logical Representations

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      Book Chapter

      Pages 4-21

      Beyond Prediction: Directions for Probabilistic and Relational Learning

  3. Extended Abstracts

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      Book Chapter

      Pages 22-23

      Learning Probabilistic Logic Models from Probabilistic Examples (Extended Abstract)

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      Book Chapter

      Pages 24-24

      Learning Directed Probabilistic Logical Models Using Ordering-Search

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      Book Chapter

      Pages 25-26

      Learning to Assign Degrees of Belief in Relational Domains

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      Book Chapter

      Pages 27-28

      Bias/Variance Analysis for Relational Domains

  4. Full Papers

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      Book Chapter

      Pages 29-38

      Induction of Optimal Semantic Semi-distances for Clausal Knowledge Bases

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      Book Chapter

      Pages 39-48

      Clustering Relational Data Based on Randomized Propositionalization

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      Book Chapter

      Pages 49-62

      Structural Statistical Software Testing with Active Learning in a Graph

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      Book Chapter

      Pages 63-77

      Learning Declarative Bias

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      Book Chapter

      Pages 78-87

      ILP :- Just Trie It

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      Book Chapter

      Pages 88-97

      Learning Relational Options for Inductive Transfer in Relational Reinforcement Learning

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      Book Chapter

      Pages 98-111

      Empirical Comparison of “Hard” and “Soft” Label Propagation for Relational Classification

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      Book Chapter

      Pages 112-121

      A Phase Transition-Based Perspective on Multiple Instance Kernels

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      Book Chapter

      Pages 122-131

      Combining Clauses with Various Precisions and Recalls to Produce Accurate Probabilistic Estimates

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      Book Chapter

      Pages 132-146

      Applying Inductive Logic Programming to Process Mining

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      Book Chapter

      Pages 147-160

      A Refinement Operator Based Learning Algorithm for the $\mathcal{ALC}$ Description Logic

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      Book Chapter

      Pages 161-174

      Foundations of Refinement Operators for Description Logics

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      Book Chapter

      Pages 175-190

      A Relational Hierarchical Model for Decision-Theoretic Assistance

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      Book Chapter

      Pages 191-199

      Using Bayesian Networks to Direct Stochastic Search in Inductive Logic Programming

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