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CORR
2012
Springer
187views Education» more  CORR 2012»
12 years 4 months ago
Sequential Inference for Latent Force Models
Latent force models (LFMs) are hybrid models combining mechanistic principles with non-parametric components. In this article, we shall show how LFMs can be equivalently formulate...
Jouni Hartikainen, Simo Särkkä
ICCV
1999
IEEE
14 years 10 months ago
A Dynamic Bayesian Network Approach to Figure Tracking using Learned Dynamic Models
The human figure exhibits complex and rich dynamic behavior that is both nonlinear and time-varying. However, most work on tracking and synthesizing figure motion has employed eit...
Vladimir Pavlovic, James M. Rehg, Tat-Jen Cham, Ke...
NECO
2008
134views more  NECO 2008»
13 years 8 months ago
Latent Features in Similarity Judgments: A Nonparametric Bayesian Approach
One of the central problems in cognitive science is determining the mental representations that underlie human inferences. Solutions to this problem often rely on the analysis of ...
Daniel J. Navarro, Thomas L. Griffiths
CVPR
2000
IEEE
14 years 10 months ago
Impact of Dynamic Model Learning on Classification of Human Motion
The human figure exhibits complex and rich dynamic behavior that is both nonlinear and time-varying. However, most work on tracking and analysis of figure motion has employed eith...
Vladimir Pavlovic, James M. Rehg
NN
1997
Springer
174views Neural Networks» more  NN 1997»
14 years 21 days 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