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IJCNN
2006
IEEE
14 years 1 months ago
Improving the Convergence of Backpropagation by Opposite Transfer Functions
—The backpropagation algorithm is a very popular approach to learning in feed-forward multi-layer perceptron networks. However, in many scenarios the time required to adequately ...
Mario Ventresca, Hamid R. Tizhoosh
IJON
2000
71views more  IJON 2000»
13 years 7 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
GECCO
2007
Springer
201views Optimization» more  GECCO 2007»
14 years 2 months ago
Evolving explicit opponent models in game playing
Opponent models are necessary in games where the game state is only partially known to the player, since the player must infer the state of the game based on the opponent’s acti...
Alan J. Lockett, Charles L. Chen, Risto Miikkulain...
ICANN
2010
Springer
13 years 9 months ago
Unsupervised Learning of Relations
Learning processes allow the central nervous system to learn relationships between stimuli. Even stimuli from different modalities can easily be associated, and these associations ...
Matthew Cook, Florian Jug, Christoph Krautz, Angel...
IWANN
2005
Springer
14 years 1 months ago
Filtering-Free Blind Separation of Correlated Images
Abstract. When using ICA for image separation, a well-known problem is that most often a large correlation exists between the sources. Because of this dependence, there is no more ...
Frédéric Vrins, John Aldo Lee, Miche...