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» Variable selection using neural-network models
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NN
1997
Springer
174views Neural Networks» more  NN 1997»
15 years 1 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
NN
1998
Springer
14 years 9 months ago
A tennis serve and upswing learning robot based on bi-directional theory
We experimented on task-level robot learning based on bi-directional theory. The via-point representation was used for ‘learning by watching’. In our previous work, we had a r...
Hiroyuki Miyamoto, Mitsuo Kawato
IJCNN
2000
IEEE
15 years 2 months ago
Predictive Multiple Model Switching Control with the Self-Organizing Map
—A predictive, multiple model control strategy is developed by extension of self-organizing map (SOM) local dynamic modeling of nonlinear autonomous systems to a control framewor...
Mark A. Motter
BMCBI
2006
122views more  BMCBI 2006»
14 years 9 months ago
A multivariate prediction model for microarray cross-hybridization
Background: Expression microarray analysis is one of the most popular molecular diagnostic techniques in the post-genomic era. However, this technique faces the fundamental proble...
Yian A. Chen, Cheng-Chung Chou, Xinghua Lu, Elizab...
ANNPR
2006
Springer
15 years 1 months ago
Visual Classification of Images by Learning Geometric Appearances Through Boosting
We present a multiclass classification system for gray value images through boosting. The feature selection is done using the LPBoost algorithm which selects suitable features of a...
Martin Antenreiter, Christian Savu-Krohn, Peter Au...