Alex J. Cannon
Orcid: 0000-0002-8025-3790
According to our database1,
Alex J. Cannon
authored at least 14 papers
between 2005 and 2023.
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Bibliography
2023
Algorithmic Hallucinations of Near-Surface Winds: Statistical Downscaling with Generative Adversarial Networks to Convection-Permitting Scales.
CoRR, 2023
2018
2016
Bayesian Neural Networks Based Bootstrap Aggregating for Tropical Cyclone Tracks Prediction in South China Sea.
Proceedings of the Neural Information Processing - 23rd International Conference, 2016
2015
Nonlinear regression in environmental sciences using extreme learning machines: A comparative evaluation.
Environ. Model. Softw., 2015
2013
Nonlinear regression in environmental sciences by support vector machines combined with evolutionary strategy.
Comput. Geosci., 2013
2012
Corrigendum to "Quantile regression neural networks: Implementation in R and application to precipitation downscaling" [Comput. Geosci. 37(9) (2011) 1277-1284].
Comput. Geosci., 2012
Neural networks for probabilistic environmental prediction: Conditional Density Estimation Network Creation and Evaluation (CaDENCE) in R.
Comput. Geosci., 2012
Downscaling temperature and precipitation using support vector regression with evolutionary strategy.
Proceedings of the 2012 International Joint Conference on Neural Networks (IJCNN), 2012
2011
GEVcdn: An R package for nonstationary extreme value analysis by generalized extreme value conditional density estimation network.
Comput. Geosci., 2011
Quantile regression neural networks: Implementation in R and application to precipitation downscaling.
Comput. Geosci., 2011
2008
A comparison of bayesian and conditional density models in probabilistic ozone forecasting.
Proceedings of the International Joint Conference on Neural Networks, 2008
2007
Nonlinear analog predictor analysis: A coupled neural network/analog model for climate downscaling.
Neural Networks, 2007
2006
Proceedings of the International Joint Conference on Neural Networks, 2006
2005
Proceedings of the IEEE International Joint Conference on Neural Networks, 2005