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GECCO
2005
Springer
155views Optimization» more  GECCO 2005»
15 years 9 months ago
Co-evolving recurrent neurons learn deep memory POMDPs
Recurrent neural networks are theoretically capable of learning complex temporal sequences, but training them through gradient-descent is too slow and unstable for practical use i...
Faustino J. Gomez, Jürgen Schmidhuber
IDA
2010
Springer
15 years 2 months ago
Multi-dimensional data construction method with its application to learning from small-sample-sets
Insufficient training data is one of the major problems in neural network learning, because it leads to poor learning performance. In order to enhance an intelligent learning proc...
Hsiao-Fan Wang, Chun-Jung Huang
ICANN
2010
Springer
15 years 1 months ago
Learning in a Unitary Coherent Hippocampus
Abstract. A previous paper [2] presented a model (UCPF-HC) of the hippocampus as a unitary coherent particle filter, which combines the classical hippocampal roles of associative m...
Charles W. Fox, Tony J. Prescott
149
Voted
ICARCV
2008
IEEE
200views Robotics» more  ICARCV 2008»
15 years 10 months ago
A robot behavior-learning experiment using Particle Swarm Optimization for training a neural-based animat
— We investigate the use of Particle Swarm Optimization (PSO), and compare with Genetic Algorithms (GA), for a particular robot behavior-learning task: the training of an animat ...
Fabien Moutarde
AUSAI
2004
Springer
15 years 9 months ago
Genetic Algorithm Based K-Means Fast Learning Artificial Neural Network
The K-means Fast Learning Artificial Neural Network (KFLANN) is a small neural network bearing two types of parameters, the tolerance, δ and the vigilance, µ. In previous papers,...
Yin Xiang, Alex Leng Phuan Tay