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ORCIDMachine Learning, Volume 73, 2008
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 M. Domingos, Lise Getoor, Stephen H. 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 H. Muggleton, José Carlos Almeida Santos:
Learning probabilistic logic models from probabilistic examples. 55-85 - Jennifer Neville, David D. 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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