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KDD
1998
ACM
190views Data Mining» more  KDD 1998»
13 years 11 months ago
Time Series Forecasting from High-Dimensional Data with Multiple Adaptive Layers
This paper describes our work in learning online models that forecast real-valued variables in a high-dimensional space. A 3GB database was collected by sampling 421 real-valued s...
R. Bharat Rao, Scott Rickard, Frans Coetzee
NN
2006
Springer
13 years 7 months ago
Machine learning in sedimentation modelling
The paper presents machine learning (ML) models that predict sedimentation in the harbour basin of the Port of Rotterdam. The important factors affecting the sedimentation process...
Biswanath Bhattacharya, Dimitri P. Solomatine
3DPH
2009
128views Healthcare» more  3DPH 2009»
13 years 8 months ago
Predicting Missing Markers in Real-Time Optical Motion Capture
Abstract. A common problem in optical motion capture of human-body movement is the so-called missing marker problem. The occlusion of markers can lead to significant problems in tr...
Tommaso Piazza, Johan Lundström, Andreas Kunz...
ASC
2000
13 years 8 months ago
Extended Neural Model Predictive Control of Non-Linear Systems
A neural model-based predictive control scheme is proposed for dealing with steady-state offsets found in standard MPC schemes. This structure is based on a constrained local inst...
P. Gil, J. Henriques, A. Dourado, H. Duarte-Ramos
PKDD
2009
Springer
155views Data Mining» more  PKDD 2009»
14 years 2 months ago
Dynamic Factor Graphs for Time Series Modeling
Abstract. This article presents a method for training Dynamic Factor Graphs (DFG) with continuous latent state variables. A DFG includes factors modeling joint probabilities betwee...
Piotr W. Mirowski, Yann LeCun