17. ILP 2007: Corvallis, Oregon, USA

Invited Talks

• Paolo Frasconi:
  Learning with Kernels and Logical Representations. 1-3
  
• David D. Jensen:
  Beyond Prediction: Directions for Probabilistic and Relational Learning. 4-21
  

Extended Abstracts

• Jianzhong Chen, Stephen Muggleton, Jose Santos:
  Learning Probabilistic Logic Models from Probabilistic Examples (Extended Abstract). 22-23
  
• Daan Fierens, Jan Ramon, Maurice Bruynooghe, Hendrik Blockeel:
  Learning Directed Probabilistic Logical Models Using Ordering-Search. 24
  
• Frédéric Koriche:
  Learning to Assign Degrees of Belief in Relational Domains. 25-26
  
• Jennifer Neville, David Jensen:
  Bias/Variance Analysis for Relational Domains. 27-28
  

Full Papers

• Claudia d'Amato, Nicola Fanizzi, Floriana Esposito:
  Induction of Optimal Semantic Semi-distances for Clausal Knowledge Bases. 29-38
  
• Grant Anderson, Bernhard Pfahringer:
  Clustering Relational Data Based on Randomized Propositionalization. 39-48
  
• Nicolas Baskiotis, Michèle Sebag:
  Structural Statistical Software Testing with Active Learning in a Graph. 49-62
  
• Will Bridewell, Ljupco Todorovski:
  Learning Declarative Bias. 63-77
  
• Rui Camacho, Nuno A. Fonseca, Ricardo Rocha, Vítor Santos Costa:
  ILP : - Just Trie It. 78-87
  
• Tom Croonenborghs, Kurt Driessens, Maurice Bruynooghe:
  Learning Relational Options for Inductive Transfer in Relational Reinforcement Learning. 88-97
  
• Aram Galstyan, Paul R. Cohen:
  Empirical Comparison of "Hard" and "Soft" Label Propagation for Relational Classification. 98-111
  
• Romaric Gaudel, Michèle Sebag, Antoine Cornuéjols:
  A Phase Transition-Based Perspective on Multiple Instance Kernels. 112-121
  
• Mark Goadrich, Jude W. Shavlik:
  Combining Clauses with Various Precisions and Recalls to Produce Accurate Probabilistic Estimates. 122-131
  
• Evelina Lamma, Paola Mello, Fabrizio Riguzzi, Sergio Storari:
  Applying Inductive Logic Programming to Process Mining. 132-146
  
• Jens Lehmann, Pascal Hitzler:
  A Refinement Operator Based Learning Algorithm for the ALC Description Logic. 147-160
  
• Jens Lehmann, Pascal Hitzler:
  Foundations of Refinement Operators for Description Logics. 161-174
  
• Sriraam Natarajan, Prasad Tadepalli, Alan Fern:
  A Relational Hierarchical Model for Decision-Theoretic Assistance. 175-190
  
• Louis Oliphant, Jude W. Shavlik:
  Using Bayesian Networks to Direct Stochastic Search in Inductive Logic Programming. 191-199
  
• Aline Paes, Gerson Zaverucha, Vítor Santos Costa:
  Revising First-Order Logic Theories from Examples Through Stochastic Local Search. 200-210
  
• Ganesh Ramakrishnan, Sachindra Joshi, Sreeram Balakrishnan, Ashwin Srinivasan:
  Using ILP to Construct Features for Information Extraction from Semi-structured Text. 211-224
  
• Oliver Ray, Katsumi Inoue:
  Mode-Directed Inverse Entailment for Full Clausal Theories. 225-238
  
• Yosuke Sasaki, Hitoshi Yamasaki, Takayoshi Shoudai, Tomoyuki Uchida:
  Mining of Frequent Block Preserving Outerplanar Graph Structured Patterns. 239-253
  
• Lisa Torrey, Jude W. Shavlik, Trevor Walker, Richard Maclin:
  Relational Macros for Transfer in Reinforcement Learning. 254-268
  
• Anneleen Van Assche, Hendrik Blockeel:
  Seeing the Forest Through the Trees. 269-279
  
• Trevor Walker, Lisa Torrey, Jude W. Shavlik, Richard Maclin:
  Building Relational World Models for Reinforcement Learning. 280-291
  
• Xiaobing Wu:
  An Inductive Learning System for XML Documents. 292-306
  

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