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» Approximate Learning of Dynamic Models
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152
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NN
2010
Springer
225views Neural Networks» more  NN 2010»
15 years 1 months ago
Learning to imitate stochastic time series in a compositional way by chaos
This study shows that a mixture of RNN experts model can acquire the ability to generate sequences that are combination of multiple primitive patterns by means of self-organizing ...
Jun Namikawa, Jun Tani
118
Voted
GLOBECOM
2007
IEEE
15 years 9 months ago
ARMA Synthesis of Fading Channels- an Application to the Generation of Dynamic MIMO Channels
— Adaptive transceivers play an important role in wireless communications and the design of MIMO systems. Therefore models that enable simulation of dynamic and time varying chan...
Hani Mehrpouyan, Steven D. Blostein
117
Voted
ICML
2009
IEEE
16 years 3 months ago
Prototype vector machine for large scale semi-supervised learning
Practical data mining rarely falls exactly into the supervised learning scenario. Rather, the growing amount of unlabeled data poses a big challenge to large-scale semi-supervised...
Kai Zhang, James T. Kwok, Bahram Parvin
CVPR
2005
IEEE
16 years 4 months ago
A Caratheodory-Fejer Approach to Dynamic Appearance Modeling
This paper presents a technique to learn dynamic appearance models from a small number of training frames. Under this framework, dynamic appearance is modelled as an unknown opera...
Hwasup Lim, Octavia I. Camps, Mario Sznaier
114
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ICASSP
2009
IEEE
15 years 9 months ago
Extended VTS for noise-robust speech recognition
Model compensation is a standard way of improving the robustness of speech recognition systems to noise. A number of popular schemes are based on vector Taylor series (vts) compen...
Rogier C. van Dalen, Mark J. F. Gales