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NN
1997
Springer
174views Neural Networks» more  NN 1997»
15 years 10 months ago
Learning Dynamic Bayesian Networks
Bayesian networks are directed acyclic graphs that represent dependencies between variables in a probabilistic model. Many time series models, including the hidden Markov models (H...
Zoubin Ghahramani
CDC
2010
IEEE
155views Control Systems» more  CDC 2010»
15 years 1 months ago
Linear parametric noise models for Least Squares Support Vector Machines
In the identification of nonlinear dynamical models it may happen that not only the system dynamics have to be modeled but also the noise has a dynamic character. We show how to ad...
Tillmann Falck, Johan A. K. Suykens, Bart De Moor
CAISE
2009
Springer
16 years 1 months ago
Complexity Levels of Representing Dynamics in EA Planning
Abstract. Enterprise Architecture (EA) models provide information on the fundamental as-is structure of a company or governmental agency and thus serve as an informational basis fo...
Stephan Aier, Bettina Gleichauf, Jan Saat, Robert ...
CA
2002
IEEE
15 years 11 months ago
A Hybrid Dynamical Systems Approach to Intelligent Low-Level Navigation
Animated characters may exhibit several kinds of dynamic intelligence when performing low-level navigation (i.e., navigation on a local perceptual scale): They decide among differ...
Eric Aaron, Harold C. Sun, Franjo Ivancic, Dimitri...
TODAES
2008
104views more  TODAES 2008»
15 years 6 months ago
Wavelet-based dynamic power management for nonstationary service requests
The goal of dynamic power management is to reduce power dissipation in system level by putting system components into different states. This paper proposes a wavelet based approac...
Ali Abbasian, Safar Hatami, Ali Afzali-Kusha, Mass...