The Strength of Weak Learnability
Machine Learning
Original Contribution: Stacked generalization
Neural Networks
Hierarchical mixtures of experts and the EM algorithm
Neural Computation
Machine Learning
Ensemble learning via negative correlation
Neural Networks
Fully Complex Multi-Layer Perceptron Network for Nonlinear Signal Processing
Journal of VLSI Signal Processing Systems
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Complex-Valued Neural Networks (Studies in Computational Intelligence)
Complex-Valued Neural Networks (Studies in Computational Intelligence)
Classifier ensembles: Select real-world applications
Information Fusion
Adaptive mixtures of local experts
Neural Computation
Evolutionary ensembles with negative correlation learning
IEEE Transactions on Evolutionary Computation
A constructive algorithm for training cooperative neural network ensembles
IEEE Transactions on Neural Networks
Fast learning fully complex-valued classifiers for real-valued classification problems
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part I
A novel approach to classificatory problem using neuro-fuzzy architecture
International Journal of Systems, Control and Communications
Comparing classifiers and metaclassifiers
ICDM'11 Proceedings of the 11th international conference on Advances in data mining: applications and theoretical aspects
Information Sciences: an International Journal
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This paper presents ensemble approaches in single-layered complex-valued neural network (CVNN) to solve real-valued classification problems. Each component CVNN of an ensemble uses a recently proposed activation function for its complex-valued neurons (CVNs). A gradient-descent based learning algorithm was used to train the component CVNNs. We applied two ensemble methods, negative correlation learning and bagging, to create the ensembles. Experimental results on a number of real-world benchmark problems showed a substantial performance improvement of the ensembles over the individual single-layered CVNN classifiers. Furthermore, the generalization performances were nearly equivalent to those obtained by the ensembles of real-valued multilayer neural networks.