Neural networks and the bias/variance dilemma
Neural Computation
Hierarchical mixtures of experts and the EM algorithm
Neural Computation
Machine Learning
Neural Networks - 2003 Special issue: Neural network analysis of complex scientific data: Astronomy and geosciences
Adaptive mixtures of local experts
Neural Computation
An empirical evaluation of bagging and boosting
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Stability problems with artificial neural networks and the ensemble solution
Artificial Intelligence in Medicine
2006 Special issue: Modular learning models in forecasting natural phenomena
Neural Networks - 2006 special issue: Earth sciences and environmental applications of computational intelligence
Parallel training of artificial neural networks using multithreaded and multicore CPUs
ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part I
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The new generation of earth observation satellites carries advanced sensors that will gather very precise data for studying the Earth system and global climate. This paper shows that neural network methods can be successfully used for solving forward and inverse remote sensing problems, providing both accurate and fast solutions. Two examples of multi-neural network systems for the determination of cloud properties and for the retrieval of total columns of ozone using satellite data are presented. The developed algorithms based on multi-neural network are currently being used for the operational processing of European atmospheric satellite sensors and will play a key role in related satellite missions planed for the near future.