Fuzzy Sets and Systems - Featured Issue: Selected papers from ACIDCA 2000
Combination of multiple classifiers for the customer's purchase behavior prediction
Decision Support Systems - Special issue: Agents and e-commerce business models
Confidence Combination Methods in Multi-expert Systems
Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Ensembles of Learning Machines
WIRN VIETRI 2002 Proceedings of the 13th Italian Workshop on Neural Nets-Revised Papers
Classifier Fusion Using Local Confidence
ISMIS '02 Proceedings of the 13th International Symposium on Foundations of Intelligent Systems
Combining Multiple Classifiers in Probabilistic Neural Networks
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Consensus Based Classification of Multisource Remote Sensing Data
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Information Analysis of Multiple Classifier Fusion
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
Interval Generalization of the Bayesian Model of Collective Decision-Making in Conflict Situations
Cybernetics and Systems Analysis
Fuzzy integral-based perceptron for two-class pattern classification problems
Information Sciences: an International Journal
Robust fuzzy relational classifier incorporating the soft class labels
Pattern Recognition Letters
Nonlinear Complex Neural Circuits Analysis and Design by q-Value Weighted Bounded Operator
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
Real-Time Road Sign Detection Using Fuzzy-Boosting
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Study of Facial Features Combination Using a Novel Adaptive Fuzzy Integral Fusion Model
IEICE - Transactions on Information and Systems
A new technique for combining multiple classifiers using the dempster-shafer theory of evidence
Journal of Artificial Intelligence Research
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Multiple classifier fusion using k-nearest localized templates
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Neuro-fuzzy-combiner: an effective multiple classifier system
International Journal of Knowledge Engineering and Soft Data Paradigms
Fuzzy integral based data fusion for protein function prediction
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part I
Combination of multiple nearest neighbor classifiers based on feature subset clustering method
ICMLC'05 Proceedings of the 4th international conference on Advances in Machine Learning and Cybernetics
Some characteristics of fuzzy integrals as a multiple classifiers fusion method
ICMLC'05 Proceedings of the 4th international conference on Advances in Machine Learning and Cybernetics
Multi-class probability SVM fusion using fuzzy integral for fault diagnosis
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
Remote sensing image classification: a neuro-fuzzy MCS approach
ICVGIP'06 Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing
A fuzzy classifier to deal with similarity between labels on automatic prosodic labeling
Computer Speech and Language
Combining classifiers using nearest decision prototypes
Applied Soft Computing
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Multiplayer feedforward networks trained by minimizing the mean squared error and by using a one of c teaching function yield network outputs that estimate posterior class probabilities. This provides a sound basis for combining the results from multiple networks to get more accurate classification. This paper presents a method for combining multiple networks based on fuzzy logic, especially the fuzzy integral. This method non-linearly combines objective evidence, in the form of a network output, with subjective evaluation of the importance of the individual neural networks. The experimental results with the recognition problem of on-line handwriting characters show that the performance of individual networks could be improved significantly