Boosted mixture of experts: an ensemble learning scheme
Neural Computation
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Adaptive mixtures of local experts
Neural Computation
Combining classifier with a fuser implemented as a one layer perceptron
ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part II
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The paper presents novel algorithm of decision making in multiple classifier system (MCS), which response is based on weighted fusion of discriminating functions derived from a pool of elementary classifiers Radial basis function model are used to establish the weights of the classifiers over a feature space For best exploitation of knowledge collected by the classifiers parameters of the weight functions are set during learning process of the MCS that aims at minimizing misclassification rate of the MCS Quality of the proposed radial basis function MCS (RB MCS) is verified in the set of experiments carried out on the set of benchmark datasets derived from UCI repository.