The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Ensembling neural networks: many could be better than all
Artificial Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computational Statistics & Data Analysis - Nonlinear methods and data mining
Stacking Bagged and Dagged Models
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Clustering ensembles of neural network models
Neural Networks
Pose Invariant Face Recognition
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Support Vector Machines for Pattern Classification (Advances in Pattern Recognition)
Support Vector Machines for Pattern Classification (Advances in Pattern Recognition)
Rotation Forest: A New Classifier Ensemble Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparison studies on classification for remote sensing image based on data mining method
WSEAS Transactions on Computers
Monitoring event-based suspended sediment concentration by artificial neural network models
WSEAS Transactions on Computers
WSEAS Transactions on Computers
Neural network ensembles: evaluation of aggregation algorithms
Artificial Intelligence
AICI '09 Proceedings of the 2009 International Conference on Artificial Intelligence and Computational Intelligence - Volume 01
Municipal revenue prediction by support vector machine ensembles
ICCOMP'10 Proceedings of the 14th WSEAS international conference on Computers: part of the 14th WSEAS CSCC multiconference - Volume I
EUS SVMs: ensemble of under-sampled SVMs for data imbalance problems
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
Cooperative coevolution of artificial neural network ensembles for pattern classification
IEEE Transactions on Evolutionary Computation
Medical diagnosis with C4.5 rule preceded by artificial neural network ensemble
IEEE Transactions on Information Technology in Biomedicine
Lung cancer cell identification based on artificial neural network ensembles
Artificial Intelligence in Medicine
Stability problems with artificial neural networks and the ensemble solution
Artificial Intelligence in Medicine
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Municipalities have to to pay increasing attention to the importance of revenue prediction due to fiscal stress. Currently, judgmental, extrapolative, and deterministic models are used for municipal revenue prediction. In this paper we present the designs of neural network and support vector machine ensembles for a real-world regression problem, i.e. prediction of municipal revenue. Base learners, as well as linear regression models are used as benchmark methods. We prove that there is no single ensemble method suitable for this regression problem. However, the ensembles of support vector machines and neural networks outperformed the base learners and linear regression models significantly.