Methods for combining experts' probability assessments
Neural Computation
Combination of Multiple Classifiers Using Local Accuracy Estimates
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine 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)
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
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Preliminary Study on Optimization of Data Distribution in Resource Sharing Systems
ICSENG '08 Proceedings of the 2008 19th International Conference on Systems Engineering
Some Remarks on Chosen Methods of Classifier Fusion Based on Weighted Voting
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
Bayesian analysis of linear combiners
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Multiple classifier system with radial basis weight function
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
Designing fusers on the basis of discriminants – evolutionary and neural methods of training
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
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The combining approach to classification so-called Multiple Classifier Systems (MCSs) is nowadays one of the most promising directions in pattern recognition and gained a lot of interest through recent years. A large variety of methods that exploit the strengths of individual classifiers have been developed. The most popular methods have their origins in voting, where the decision of a common classifier is a combination of individual classifiers' outputs, i.e. class numbers or values of discriminants. Of course to improve performance and robustness of compound classifiers, different and diverse individual classifiers should be combined. This work focuses on the problem of fuser design. We present some new results of our research and propose to train a fusion block by algorithms that have their origin in neural computing. As we have shown in previous works, we can produce better results combining classifiers than by using the abstract model of fusion so-called Oracle. The results of our experiments are presented to confirm our previous observations.