Generalized Regression Neural Networks with K-Fold Cross-Validation for Displacement of Landslide Forecasting

2014 ◽  
pp. 533-541 ◽  
Author(s):  
Ping Jiang ◽  
Zhigang Zeng ◽  
Jiejie Chen ◽  
Tingwen Huang
Keyword(s):  
Neural Networks ◽  
Cross Validation ◽  
Generalized Regression Neural Networks ◽  
Landslide Forecasting ◽  
Generalized Regression ◽  
Fold Cross Validation

scholarly journals Adaptive Trajectory Tracking Control for Quadrotors with Disturbances by Using Generalized Regression Neural Networks

2021 ◽  
Author(s):  
Ivan Lopez-Sanchez ◽  
Francisco Rossomando ◽  
Ricardo Pérez-Alcocer ◽  
Carlos Soria ◽  
Ricardo Carelli ◽  
...  
Keyword(s):  
Neural Networks ◽  
Trajectory Tracking ◽  
Tracking Control ◽  
Trajectory Tracking Control ◽  
Generalized Regression Neural Networks ◽  
Generalized Regression

Model selection for direct marketing: performance criteria and validation methods

2008 ◽  
Vol 26 (3) ◽  
pp. 275-292 ◽  
Author(s):  
Geng Cui ◽  
Man Leung Wong ◽  
Guichang Zhang ◽  
Lin Li
Keyword(s):  
Neural Networks ◽  
Model Selection ◽  
Cross Validation ◽  
Direct Marketing ◽  
Latent Class Models ◽  
Performance Criteria ◽  
Content Type ◽  
Validation Methods ◽  
Practical Implications ◽  
Fold Cross Validation

PurposeThe purpose of this paper is to assess the performance of competing methods and model selection, which are non‐trivial issues given the financial implications. Researchers have adopted various methods including statistical models and machine learning methods such as neural networks to assist decision making in direct marketing. However, due to the different performance criteria and validation techniques currently in practice, comparing different methods is often not straightforward.Design/methodology/approachThis study compares the performance of neural networks with that of classification and regression tree, latent class models and logistic regression using three criteria – simple error rate, area under the receiver operating characteristic curve (AUROC), and cumulative lift – and two validation methods, i.e. bootstrap and stratified k‐fold cross‐validation. Systematic experiments are conducted to compare their performance.FindingsThe results suggest that these methods vary in performance across different criteria and validation methods. Overall, neural networks outperform the others in AUROC value and cumulative lifts, and the stratified ten‐fold cross‐validation produces more accurate results than bootstrap validation.Practical implicationsTo select predictive models to support direct marketing decisions, researchers need to adopt appropriate performance criteria and validation procedures.Originality/valueThe study addresses the key issues in model selection, i.e. performance criteria and validation methods, and conducts systematic analyses to generate the findings and practical implications.

Discussion of “Generalized regression neural networks for evapotranspiration modelling”

2007 ◽  
Vol 52 (4) ◽  
pp. 825-831 ◽  
Author(s):  
Hafzullah Aksoy ◽  
Aytac Guven ◽  
Ali Aytek ◽  
M. Ishak Yuce ◽  
N. Erdem Unal
Keyword(s):  
Neural Networks ◽  
Generalized Regression Neural Networks ◽  
Generalized Regression

Corporate reputation and market value: Evidence with generalized regression neural networks

2016 ◽  
Vol 46 ◽  
pp. 69-76 ◽  
Author(s):  
Manuel A. Fernández-Gámez ◽  
Antonio M. Gil-Corral ◽  
Federico Galán-Valdivieso
Keyword(s):  
Neural Networks ◽  
Corporate Reputation ◽  
Market Value ◽  
Generalized Regression Neural Networks ◽  
Generalized Regression
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