The Strength of Weak Learnability
Machine Learning
Optimal linear combinations of neural networks
Optimal linear combinations of neural networks
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Optimization of entropy with neural networks
Optimization of entropy with neural networks
Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems
Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Design of structural modular neural networks with genetic algorithm
Advances in Engineering Software
Information Sciences: an International Journal - Special issue: Information technology
Neural network ensemble strategies for financial decision applications
Computers and Operations Research
2005 Special Issue: Cross-entropy embedding of high-dimensional data using the neural gas model
Neural Networks - 2005 Special issue: IJCNN 2005
Flexible neural trees ensemble for stock index modeling
Neurocomputing
Weather analysis using ensemble of connectionist learning paradigms
Applied Soft Computing
Design of ensemble neural network using the Akaike information criterion
Engineering Applications of Artificial Intelligence
Predicting software reliability with neural network ensembles
Expert Systems with Applications: An International Journal
Simulating the seismic response of embankments via artificial neural networks
Advances in Engineering Software
A novel nonlinear neural network ensemble model for financial time series forecasting
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part I
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Ensemble neural networks (ENNs) are commonly used neural networks in many engineering applications due to their better generalization properties. An ENN usually includes several component networks in its structure, and each component network commonly uses a single feed-forward network trained with the back-propagation learning rule. As the neural network architecture has a significant influence on its generalization ability, it is crucial to develop a proper algorithm to determine the ENN architecture. In this paper, an ENN, which combines the component networks using the entropy theory, is proposed. The entropy-based ENN searches the best structure of each component network first, and employs entropy as an automating design tool to determine the best combining weights. Two analytical functions - the peak function and the Friedman function are used to assess the accuracy of the proposed ensemble approach. Then, the entropy-based ENN is applied to the modeling of peak particle velocity (PPV) damage criterion for rock mass. These computational experiments have verified that the proposed entropy-based ENN outperforms the simple averaging ENN and the single NN.