Decision Combination in Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Methods for combining experts' probability assessments
Neural Computation
Optimal combinations of pattern classifiers
Pattern Recognition Letters
Fusion of handwritten word classifiers
Pattern Recognition Letters - Special issue on fuzzy set technology in pattern recognition
Soft combination of neural classifiers: a comparative study
Pattern Recognition Letters
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems
Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Adaptive mixtures of local experts
Neural Computation
Decisions and evaluations by hierarchical aggregation of information
Fuzzy Sets and Systems
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In this paper we propose two new ensemble combiners based on the Mixture of Neural Networksmodel. In our experiments, we have applied two different network architectures on the methods based on the Mixture of Neural Networks: the Basic Network(BN) and the Multilayer Feedforward Network(MF). Moreover, we have used ensembles of MFnetworks previously trained with Simple Ensembleto test the performance of the combiners we propose. Finally, we compare the mixture combinersproposed with three different mixture models and other traditional combiners. The results show that the mixture combiners proposed are the best way to build Multi-net systems among the methods studied in the paper in general.