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ECCV
2010
Springer
15 years 6 months ago
MIForests: Multiple-Instance Learning with Randomized Trees
Abstract. Multiple-instance learning (MIL) allows for training classifiers from ambiguously labeled data. In computer vision, this learning paradigm has been recently used in many ...
Christian Leistner, Amir Saffari, Horst Bischof
152
Voted
INFORMATICALT
2006
88views more  INFORMATICALT 2006»
15 years 6 months ago
Improving the Performances of Asynchronous Algorithms by Combining the Nogood Processors with the Nogood Learning Techniques
Abstract. The asynchronous techniques that exist within the programming with distributed constraints are characterized by the occurrence of the nogood values during the search for ...
Ionel Muscalagiu, Vladimir Cretu
ICRA
2010
IEEE
158views Robotics» more  ICRA 2010»
15 years 4 months ago
Movement templates for learning of hitting and batting
Abstract— Hitting and batting tasks, such as tennis forehands, ping-pong strokes, or baseball batting, depend on predictions where the ball can be intercepted and how it can prop...
Jens Kober, Katharina Mülling, Oliver Kroemer...
SDM
2012
SIAM
252views Data Mining» more  SDM 2012»
13 years 8 months ago
Learning from Heterogeneous Sources via Gradient Boosting Consensus
Multiple data sources containing different types of features may be available for a given task. For instance, users’ profiles can be used to build recommendation systems. In a...
Xiaoxiao Shi, Jean-François Paiement, David...
IPMI
2007
Springer
16 years 7 months ago
Learning Best Features and Deformation Statistics for Hierarchical Registration of MR Brain Images
A fully learning-based framework has been presented for deformable registration of MR brain images. In this framework, the entire brain is first adaptively partitioned into a numbe...
Guorong Wu, Feihu Qi, Dinggang Shen