Optimal linear combinations of neural networks
Neural Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sum Versus Vote Fusion in Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ensembles of Similarity-based Models
Proceedings of the International Symposium on "Intelligent Information Systems X"
Experiments with Classifier Combining Rules
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Optimizing a Multiple Classifier System
PRICAI '02 Proceedings of the 7th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
An Overview and Comparison of Voting Methods for Pattern Recognition
IWFHR '02 Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition (IWFHR'02)
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
A Theoretical and Experimental Analysis of Linear Combiners for Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Bayesian analysis of linear combiners
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Designing classifier fusion systems by genetic algorithms
IEEE Transactions on Evolutionary Computation
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The Multiple Classifier Systemsare nowadays one of the most promising directions in pattern recognition. There are many methods of decision making by the ensemble of classifiers. The most popular are methods that have their origin in voting method, where the decision of the common classifier is a combination of individual classifiers' decisions. This work presents methods of classifier combination, where neural networks plays a role of fuser block. Fusion on level of recognizer responses or values of their discriminant functions is applied. The qualities of proposed methods are evaluated via computer experiments on generated data and two benchmark databases.