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ICCV
2007
IEEE
14 years 6 months ago
DynamicBoost: Boosting Time Series Generated by Dynamical Systems
Boosting is a remarkably simple and flexible classification algorithm with widespread applications in computer vision. However, the application of boosting to nonEuclidean, infini...
René Vidal, Paolo Favaro
NN
2010
Springer
225views Neural Networks» more  NN 2010»
13 years 2 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
NPL
2011
12 years 7 months ago
A Neural Network Scheme for Long-Term Forecasting of Chaotic Time Series
The accuracy of a model to forecast a time series diminishes as the prediction horizon increases, in particular when the prediction is carried out recursively. Such decay is faster...
Pilar Gómez-Gil, Juan Manuel Ramírez...
SIGPRO
2008
136views more  SIGPRO 2008»
13 years 4 months ago
Estimation of slowly varying parameters in nonlinear systems via symbolic dynamic filtering
This paper introduces a novel method for real-time estimation of slowly varying parameters in nonlinear dynamical systems. The core concept is built upon the principles of symboli...
Venkatesh Rajagopalan, Subhadeep Chakraborty, Asok...
ABIALS
2008
Springer
13 years 10 months ago
The Cognitive Body: From Dynamic Modulation to Anticipation
Abstract— Starting from the situated and embodied perspective on the study of biological cognition as a source of inspiration, this paper programmatically outlines a path towards...
Alberto Montebelli, Robert Lowe, Tom Ziemke