Combining diverse neural nets

Keywords

Redundancy (engineering), Computer science, Artificial neural network, Hierarchy, Machine learning

References

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• Cognitive modeling: psychology and connectionismAmanda Sharkey, Noel Sharkey — 1998
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• On Combining Artificial Neural NetsAmanda Sharkey — 1996
• Effects of Collinearity on Combining Neural NetworksS. Hashem — 1996
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• Dependable, intelligent voting for real-time control softwarePeter Croll, Amanda Sharkey, Julian M. Bass — 1995
• Fault diagnosis for the Space Shuttle main engineAhmet Duyar, Walter C. Merrill — 1992
• STRUCTURAL FAULT DETECTION USING A NOVELTY MEASUREKeith Worden — 1997
• Bootstrapping with Noise: An Effective Regularization TechniqueYuval Raviv, Nathan Intrator — 1996
• An Analysis of Catastrophic InterferenceNoel Sharkey, Amanda Sharkey — 1995
• Error Correlation and Error Reduction in Ensemble ClassifiersKagan Tumer, Joydeep Ghosh — 1996
• Combining the results of several neural network classifiersGalina L. Rogova — 1994
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• Neural Networks and the Bias/Variance DilemmaStuart Geman, Elie Bienenstock, René Doursat — 1992
• Boosting and Other Ensemble MethodsHarris Drucker, Corinna Cortes, L. D. Jackel — 1994
• Synergy of Clustering Multiple Back Propagation NetworksWilliam Lincoln, Josef Skrzypek — 1989
• Neural Network Ensembles, Cross Validation, and Active LearningAnders Krogh, Jesper Vedelsby — 1994

Cited by

• Measuring Diversity in Regression EnsemblesHaimonti Dutta — 2009
• Sequential decision fusion of multibiometrics applied to text-dependent speaker verification for controlled errorsVishnu Priya Nallagatla — 2012
• Ensembles of diverse neural networksM. A. H. Akhand — 2009
• A Minimal Neural Network Ensemble Construction Method: A Constructive ApproachM. A. H. Akhand, Kazuyuki Murase — 2007
• Looking inside ensembles of negatively correlated Self-Organizing MapsAlexandra Scherbart — 2009
• The application of black box models to combustion processes in the internal combustion engineBastian Maass — 2011
• Combinations of time series forecasts : when and why are they beneficial?Christiane Lemke — 2010
• Multiple Classifier SystemsFabio Roli, Josef Kittler — 2002
• An Overview of Classifier Fusion MethodsDymitr Ruta, Bogdan Gabryś — 2000
• Knowledge extraction from neural networksAntony Browne, Brian D. Hudson, David S. Whitley — 2003
• Local discriminant basis neural network ensembles with cross-validationWidhyakorn Asdornwised — 2000
• Boosted Pre-loaded Mixture of Experts for low-resolution face recognitionReza Ebrahimpour, Naser Sadeghnejad, Saeed Masoudnia — 2012
• Analysis of the Correlation Between Majority Voting Error and the Diversity Measures in Multiple Classifier SystemsDymitr Ruta, Bogdan Gabryś — 2001
• Random forests for industrial device functioning diagnostics using wireless sensor networksWiem Elghazel, Kamal Medjaher, Noureddine Zerhouni — 2015
• Speciated neural networks evolved with fitness sharing techniqueJoon-Hyun Ahn, S. B. Cho — 2001
• Multi-Classifier Systems: Review and a roadmap for developersRomesh Ranawana, Vasile Palade — 2006
• Sharpened graph ensemble for semi-supervised learningInae Choi, Kanghee Park, Hyunjung Shin — 2013
• Hybrid Segmentation Strategy and Multi-Agent SVMs for Corporate Risk Management in Class Imbalanced SituationsTe-Min Chang, Ching-Hui Shih, Ming-Fu Hsu — 2016
• Wireless sensor networks for Industrial health assessment based on a random forest approachWiem Elghazel — 2015
• Layered Ensemble Architecture for Time Series ForecastingM. M. Rahman, Md. Monirul Islam, Kazuyuki Murase — 2016

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