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» IT alignment: what have we learned
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ICML
1994
IEEE
13 years 11 months ago
Learning Without State-Estimation in Partially Observable Markovian Decision Processes
Reinforcement learning (RL) algorithms provide a sound theoretical basis for building learning control architectures for embedded agents. Unfortunately all of the theory and much ...
Satinder P. Singh, Tommi Jaakkola, Michael I. Jord...
CDC
2010
IEEE
106views Control Systems» more  CDC 2010»
13 years 2 months ago
Observational learning in an uncertain world
We study a model of observational learning in social networks in the presence of uncertainty about agents' type distributions. Each individual receives a private noisy signal ...
Daron Acemoglu, Munther A. Dahleh, Asuman E. Ozdag...
MICCAI
2004
Springer
14 years 8 months ago
Learning Coupled Prior Shape and Appearance Models for Segmentation
We present a novel framework for learning a joint shape and appearance model from a large set of un-labelled training examples in arbitrary positions and orientations. The shape an...
Xiaolei Huang, Zhiguo Li, Dimitris N. Metaxas
ESANN
2004
13 years 9 months ago
Speaker verification by means of ANNs
In text-dependent speaker verification the speech signals have to be time-aligned. For that purpose dynamic time warping (DTW) can be used which performs the alignment by minimizi...
Urs Niesen, Beat Pfister
CVPR
2011
IEEE
13 years 3 months ago
Learning Object Color Models from Multi-view Constraints
Color is known to be highly discriminative for many object recognition tasks, but is difficult to infer from uncontrolled images in which the illuminant is not known. Traditional...
Trevor Owens, Kate Saenko, Trevor Darrell, Ayan Ch...