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SDM
2010
SIAM
144views Data Mining» more  SDM 2010»
13 years 5 months ago
A Probabilistic Framework to Learn from Multiple Annotators with Time-Varying Accuracy
This paper addresses the challenging problem of learning from multiple annotators whose labeling accuracy (reliability) differs and varies over time. We propose a framework based ...
Pinar Donmez, Jaime G. Carbonell, Jeff Schneider
ICDE
2011
IEEE
200views Database» more  ICDE 2011»
12 years 7 months ago
Deriving probabilistic databases with inference ensembles
— Many real-world applications deal with uncertain or missing data, prompting a surge of activity in the area of probabilistic databases. A shortcoming of prior work is the assum...
Julia Stoyanovich, Susan B. Davidson, Tova Milo, V...
KDD
2012
ACM
205views Data Mining» more  KDD 2012»
11 years 6 months ago
Rank-loss support instance machines for MIML instance annotation
Multi-instance multi-label learning (MIML) is a framework for supervised classification where the objects to be classified are bags of instances associated with multiple labels....
Forrest Briggs, Xiaoli Z. Fern, Raviv Raich
ICIP
2009
IEEE
14 years 4 months ago
Learning Large Margin Likelihoods For Realtime Head Pose Tracking
We consider the problem of head tracking and pose estimation in realtime from low resolution images. Tracking and pose recognition are treated as two coupled problems in a probabi...
AAAI
2012
11 years 6 months ago
Model Learning and Real-Time Tracking Using Multi-Resolution Surfel Maps
For interaction with its environment, a robot is required to learn models of objects and to perceive these models in the livestreams from its sensors. In this paper, we propose a ...
Jörg Stückler, Sven Behnke