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ICDM
2005
IEEE
116views Data Mining» more  ICDM 2005»
16 years 11 days ago
Learning Functional Dependency Networks Based on Genetic Programming
Bayesian Network (BN) is a powerful network model, which represents a set of variables in the domain and provides the probabilistic relationships among them. But BN can handle dis...
Wing-Ho Shum, Kwong-Sak Leung, Man Leung Wong
ICRA
2005
IEEE
137views Robotics» more  ICRA 2005»
16 years 10 days ago
Learning Opportunity Costs in Multi-Robot Market Based Planners
— Direct human control of multi-robot systems is limited by the cognitive ability of humans to coordinate numerous interacting components. In remote environments, such as those e...
Jeff G. Schneider, David Apfelbaum, Drew Bagnell, ...
SIGIR
2010
ACM
15 years 10 months ago
Learning more powerful test statistics for click-based retrieval evaluation
Interleaving experiments are an attractive methodology for evaluating retrieval functions through implicit feedback. Designed as a blind and unbiased test for eliciting a preferen...
Yisong Yue, Yue Gao, Olivier Chapelle, Ya Zhang, T...
MLMTA
2007
15 years 8 months ago
GenInc: An Incremental Context-Free Grammar Learning Algorithm for Domain-Specific Language Development
- While grammar inference (or grammar induction) has found extensive application in the areas of robotics, computational biology, speech and pattern recognition, its application to...
Faizan Javed, Marjan Mernik, Barrett R. Bryant, Al...
DAGM
2010
Springer
15 years 7 months ago
Semi-supervised Learning of Edge Filters for Volumetric Image Segmentation
Abstract. For every segmentation task, prior knowledge about the object that shall be segmented has to be incorporated. This is typically performed either automatically by using la...
Margret Keuper, Robert Bensch, Karsten Voigt, Alex...