Artificial neural network ensembles and their application in pooled flood frequency analysis
暂无分享,去创建一个
Chang Shu | Donald H. Burn | D. Burn | C. Shu
[1] Yoav Freund,et al. Experiments with a New Boosting Algorithm , 1996, ICML.
[2] Anders Krogh,et al. Neural Network Ensembles, Cross Validation, and Active Learning , 1994, NIPS.
[3] Armando Freitas da Rocha,et al. Neural Nets , 1992, Lecture Notes in Computer Science.
[4] Michael Y. Hu,et al. Explaining consumer choice through neural networks: The stacked generalization approach , 2003, Eur. J. Oper. Res..
[5] Robert J. Abrahart,et al. Multi-model data fusion for river flow forecasting: an evaluation of six alternative methods based on two contrasting catchments , 2002 .
[6] R Govindaraju,et al. ARTIFICIAL NEURAL NETWORKS IN HYDROLOGY: II, HYDROLOGIC APPLICATIONS , 2000 .
[7] Alex J. Cannon,et al. Downscaling recent streamflow conditions in British Columbia, Canada using ensemble neural network models , 2002 .
[8] D. Thomas,et al. Generalization of streamflow characteristics from drainage-basin characteristics , 1970 .
[9] Thomas G. Dietterich. An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization , 2000, Machine Learning.
[10] David H. Wolpert,et al. Stacked generalization , 1992, Neural Networks.
[11] null null,et al. Artificial Neural Networks in Hydrology. II: Hydrologic Applications , 2000 .
[12] Amanda J. C. Sharkey,et al. Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems , 1999 .
[13] Darrel E. Bostow,et al. An experimental comparison of three methods of instruction in health education for cancer prevention: traditional paper prose text, passive non-interactive computer presentation and overt-interactive computer presentation , 1992 .
[14] Limsoon Wong,et al. DATA MINING TECHNIQUES , 2003 .
[15] T.,et al. Training Feedforward Networks with the Marquardt Algorithm , 2004 .
[16] V. Nguyen,et al. A comparative study of regression based methods in regional flood frequency analysis , 1999 .
[17] J. R. Wallis,et al. Regional Frequency Analysis: An Approach Based on L-Moments , 1997 .
[18] Evon M. O. Abu-Taieh,et al. Comparative Study , 2020, Definitions.
[19] Donald H. Burn,et al. Flood frequency analysis for ungauged sites using a region of influence approach , 1994 .
[20] Jie Zhang,et al. A comparison of different methods for combining multiple neural networks models , 2002, Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290).
[21] Leo Breiman,et al. Stacked regressions , 2004, Machine Learning.
[22] Leo Breiman,et al. Bagging Predictors , 1996, Machine Learning.
[23] Ewa M. Bielihska,et al. COMPARISON OF DIFFERENT METHODS , 1994 .
[24] L. Cooper,et al. When Networks Disagree: Ensemble Methods for Hybrid Neural Networks , 1992 .
[25] A. K. Pujari,et al. Data Mining Techniques , 2006 .
[26] Donald H. Burn,et al. A comparison of index flood estimation procedures for ungauged catchments , 2002 .
[27] R. Schapire. The Strength of Weak Learnability , 1990, Machine Learning.
[28] D. Opitz,et al. Popular Ensemble Methods: An Empirical Study , 1999, J. Artif. Intell. Res..
[29] R. Beverton,et al. Institute of Hydrology , 1972, Nature.
[30] Walter Cedeño,et al. On the Use of Neural Network Ensembles in QSAR and QSPR , 2002, J. Chem. Inf. Comput. Sci..
[31] Roy W. Koch,et al. Bias in Hydrologic Prediction Using Log-Transformed Regression Models , 1986 .
[32] Padraig Cunningham,et al. The NeuralBAG algorithm: optimizing generalization performance in bagged neural networks , 1999, ESANN.
[33] Donald H. Burn,et al. The use of flood regime information in regional flood frequency analysis , 2002 .
[34] Jie Zhang,et al. Developing robust non-linear models through bootstrap aggregated neural networks , 1999, Neurocomputing.
[35] Amanda J. C. Sharkey,et al. Boosting Using Neural Networks , 1999 .
[36] Peggy A. Johnson,et al. Problems with Logarithmic Transformations in Regression , 1990 .
[37] Robert J. Abrahart,et al. Multi-model data fusion for hydrological forecasting , 2001 .
[38] Harris Drucker,et al. Improving Regressors using Boosting Techniques , 1997, ICML.
[39] Heekuck Oh,et al. Neural Networks for Pattern Recognition , 1993, Adv. Comput..
[40] D J Mayer,et al. Reducing the risk of corneal graft rejection. A comparison of different methods. , 1987, Cornea.
[41] Yoshua Bengio,et al. Pattern Recognition and Neural Networks , 1995 .