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» Bootstrap for neural model selection
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CEC
2008
IEEE
14 years 4 months ago
Automatic model type selection with heterogeneous evolution: An application to RF circuit block modeling
— Many complex, real world phenomena are difficult to study directly using controlled experiments. Instead, the use of computer simulations has become commonplace as a cost effe...
Dirk Gorissen, Luciano De Tommasi, Jeroen Croon, T...
ICANN
2009
Springer
14 years 4 months ago
Selective Attention Improves Learning
Abstract. We demonstrate that selective attention can improve learning. Considerably fewer samples are needed to learn a source separation problem when the inputs are pre-segmented...
Antti Yli-Krekola, Jaakko Särelä, Harri ...
IJCNN
2007
IEEE
14 years 4 months ago
Search Strategies Guided by the Evidence for the Selection of Basis Functions in Regression
— This work addresses the problem of selecting a subset of basis functions for a model linear in the parameters for regression tasks. Basis functions from a set of candidates are...
Ignacio Barrio, Enrique Romero, Lluís Belan...
TIP
2008
344views more  TIP 2008»
13 years 9 months ago
A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications
Detection of moving objects in video streams is the first relevant step of information extraction in many computer vision applications. Aside from the intrinsic usefulness of being...
Lucia Maddalena, Alfredo Petrosino
IJON
2000
71views more  IJON 2000»
13 years 9 months ago
Variable selection using neural-network models
In this paper we propose an approach to variable selection that uses a neural-network model as the tool to determine which variables are to be discarded. The method performs a bac...
Giovanna Castellano, Anna Maria Fanelli