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ORCIDMachine Learning, Volume 100
Volume 100, Number 1, July 2015
- Gerson Zaverucha, Vítor Santos Costa
:
Guest editors' introduction: special issue on Inductive Logic Programming and on Multi-Relational Learning. 1-3 - Luc De Raedt, Angelika Kimmig:
Probabilistic (logic) programming concepts. 5-47 - Stephen H. Muggleton, Dianhuan Lin, Alireza Tamaddoni-Nezhad:
Meta-interpretive learning of higher-order dyadic datalog: predicate invention revisited. 49-73 - Tushar Khot, Sriraam Natarajan, Kristian Kersting, Jude W. Shavlik:
Gradient-based boosting for statistical relational learning: the Markov logic network and missing data cases. 75-100 - William Yang Wang, Kathryn Mazaitis, Ni Lao, William W. Cohen:
Efficient inference and learning in a large knowledge base - Reasoning with extracted information using a locally groundable first-order probabilistic logic. 101-126 - Nicola Di Mauro, Elena Bellodi, Fabrizio Riguzzi:
Bandit-based Monte-Carlo structure learning of probabilistic logic programs. 127-156
Volume 100, Numbers 2-3, September 2015
- Concha Bielza, João Gama, Alípio Jorge, Indre Zliobaite:
Guest Editors introduction: special issue of the ECMLPKDD 2015 journal track. 157-159 - Motoki Shiga, Voot Tangkaratt, Masashi Sugiyama:
Direct conditional probability density estimation with sparse feature selection. 161-182 - Theja Tulabandhula, Cynthia Rudin:
Generalization bounds for learning with linear, polygonal, quadratic and conic side knowledge. 183-216 - Irma Ravkic, Jan Ramon, Jesse Davis:
Learning relational dependency networks in hybrid domains. 217-254 - Matteo Pirotta, Marcello Restelli, Luca Bascetta:
Policy gradient in Lipschitz Markov Decision Processes. 255-283 - Giorgio Corani, Alessio Benavoli:
A Bayesian approach for comparing cross-validated algorithms on multiple data sets. 285-304 - Matthieu Geist:
Soft-max boosting. 305-332 - Parthan Kasarapu, Lloyd Allison:
Minimum message length estimation of mixtures of multivariate Gaussian and von Mises-Fisher distributions. 333-378 - Cong Leng, Jian Cheng:
Consensus hashing. 379-398 - Mohamed Elhoseiny, Ahmed M. Elgammal:
Generalized Twin Gaussian processes using Sharma-Mittal divergence. 399-424 - Amit Dhurandhar, Karthik Sankaranarayanan:
Improving classification performance through selective instance completion. 425-447 - Georg Krempl, Daniel Kottke, Vincent Lemaire:
Optimised probabilistic active learning (OPAL) - For fast, non-myopic, cost-sensitive active classification. 449-476 - Fabian Hadiji, Alejandro Molina, Sriraam Natarajan, Kristian Kersting:
Poisson Dependency Networks: Gradient Boosted Models for Multivariate Count Data. 477-507 - Heiko Paulheim, Robert Meusel:
A decomposition of the outlier detection problem into a set of supervised learning problems. 509-531 - Eugene Belilovsky, Andreas Argyriou, Gaël Varoquaux, Matthew B. Blaschko:
Convex relaxations of penalties for sparse correlated variables with bounded total variation. 533-553 - Nikos Katzouris, Alexander Artikis, Georgios Paliouras:
Incremental learning of event definitions with Inductive Logic Programming. 555-585 - Brijnesh J. Jain:
Generalized gradient learning on time series. 587-608 - Samaneh Khoshrou, Jaime S. Cardoso, Luís Filipe Teixeira:
Learning from evolving video streams in a multi-camera scenario. 609-633 - Julia E. Vogt, Marius Kloft, Stefan Stark, Sudhir Raman, Sandhya Prabhakaran, Volker Roth, Gunnar Rätsch:
Probabilistic clustering of time-evolving distance data. 635-654 - Dean S. Wookey, George Dimitri Konidaris:
Regularized feature selection in reinforcement learning. 655-676 - Bo Chen, Kai Ming Ting, Takashi Washio, Gholamreza Haffari:
Half-space mass: a maximally robust and efficient data depth method. 677-699
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