Volume 73, Number 1, October 2008

• 
  Hendrik Blockeel, Jude W. Shavlik, Prasad Tadepalli: Guest editors' introduction: special issue on inductive logic programming (ILP-2007). 1-2
• 
  Thomas G. Dietterich, Pedro Domingos, Lise Getoor, Stephen Muggleton, Prasad Tadepalli: Structured machine learning: the next ten years. 3-23
• 
  Frédéric Koriche: Learning to assign degrees of belief in relational domains. 25-53
• 
  Jianzhong Chen, Stephen Muggleton, José Carlos Almeida Santos: Learning probabilistic logic models from probabilistic examples. 55-85
• 
  Jennifer Neville, David Jensen: A bias/variance decomposition for models using collective inference. 87-106

Volume 73, Number 2, November 2008

• 
  Qingping Tao, Stephen D. Scott: Improved MCMC sampling methods for estimating weighted sums in Winnow with application to DNF learning. 107-132
• 
  Johannes Fürnkranz, Eyke Hüllermeier, Eneldo Loza Mencía, Klaus Brinker: Multilabel classification via calibrated label ranking. 133-153
• 
  Yushi Jing, Vladimir Pavlovic, James M. Rehg: Boosted Bayesian network classifiers. 155-184
• 
  Celine Vens, Jan Struyf, Leander Schietgat, Saso Dzeroski, Hendrik Blockeel: Decision trees for hierarchical multi-label classification. 185-214

Volume 73, Number 3, December 2008

• 
  Daniel L. Silver, Kristin P. Bennett: Guest editor's introduction: special issue on inductive transfer learning. 215-220
• 
  Jian Zhang, Zoubin Ghahramani, Yiming Yang: Flexible latent variable models for multi-task learning. 221-242
• 
  Andreas Argyriou, Theodoros Evgeniou, Massimiliano Pontil: Convex multi-task feature learning. 243-272
• 
  Shai Ben-David, Reba Schuller Borbely: A notion of task relatedness yielding provable multiple-task learning guarantees. 273-287
• 
  Neville Mehta, Sriraam Natarajan, Prasad Tadepalli, Alan Fern: Transfer in variable-reward hierarchical reinforcement learning. 289-312
• 
  Daniel L. Silver, Ryan Poirier, Duane Currie: Inductive transfer with context-sensitive neural networks. 313-336

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