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AI
2002
Springer
13 years 8 months ago
Ensembling neural networks: Many could be better than all
Neural network ensemble is a learning paradigm where many neural networks are jointly used to solve a problem. In this paper, the relationship between the ensemble and its compone...
Zhi-Hua Zhou, Jianxin Wu, Wei Tang
IJCNN
2006
IEEE
14 years 2 months ago
Bi-directional Modularity to Learn Visual Servoing Tasks
— This paper shows the advantage of using neural network modularity over conventional learning schemes to approximate complex functions. Indeed, it is difficult for artificial ...
Gilles Hermann, Patrice Wira, Jean-Philippe Urban
GECCO
2010
Springer
152views Optimization» more  GECCO 2010»
14 years 1 months ago
Importing the computational neuroscience toolbox into neuro-evolution-application to basal ganglia
Neuro-evolution and computational neuroscience are two scientific domains that produce surprisingly different artificial neural networks. Inspired by the “toolbox” used by ...
Jean-Baptiste Mouret, Stéphane Doncieux, Be...
NEUROSCIENCE
2001
Springer
14 years 1 months ago
Finite-State Computation in Analog Neural Networks: Steps towards Biologically Plausible Models?
Abstract. Finite-state machines are the most pervasive models of computation, not only in theoretical computer science, but also in all of its applications to real-life problems, a...
Mikel L. Forcada, Rafael C. Carrasco
NN
2000
Springer
128views Neural Networks» more  NN 2000»
13 years 8 months ago
A recurrent neural network for solving linear projection equations
Linear projection equations arise in many optimization problems and have important applications in science and engineering. In this paper, we present a recurrent neural network fo...
Youshen Xia, Jun Wang