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ATAL
2010
Springer
15 years 29 days ago
Learning multi-agent state space representations
This paper describes an algorithm, called CQ-learning, which learns to adapt the state representation for multi-agent systems in order to coordinate with other agents. We propose ...
Yann-Michaël De Hauwere, Peter Vrancx, Ann No...
ICML
2003
IEEE
16 years 19 days ago
Learning Metrics via Discriminant Kernels and Multidimensional Scaling: Toward Expected Euclidean Representation
Distance-based methods in machine learning and pattern recognition have to rely on a metric distance between points in the input space. Instead of specifying a metric a priori, we...
Zhihua Zhang
CVPR
2011
IEEE
14 years 9 months ago
Learning Better Image Representations Using 'Flobject Analysis'
Unsupervised learning can be used to extract image representations that are useful for various and diverse vision tasks. After noticing that most biological vision systems for int...
Inmar Givoni, Patrick Li, Brendan Frey
NN
2008
Springer
201views Neural Networks» more  NN 2008»
14 years 11 months ago
Learning representations for object classification using multi-stage optimal component analysis
Learning data representations is a fundamental challenge in modeling neural processes and plays an important role in applications such as object recognition. In multi-stage Optima...
Yiming Wu, Xiuwen Liu, Washington Mio
FLAIRS
2007
15 years 2 months ago
A Generalizing Spatial Representation for Robot Navigation with Reinforcement Learning
In robot navigation tasks, the representation of the surrounding world plays an important role, especially in reinforcement learning approaches. This work presents a qualitative r...
Lutz Frommberger