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Optimal linear combinations of neural networks
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Soft combination of neural classifiers: a comparative study
Pattern Recognition Letters
Optimal Linear Combination of Neural Networks for Improving Classification Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Neural Networks for Pattern Recognition
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Performance Analysis and Comparison of Linear Combiners for Classifier Fusion
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
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Pattern Classification (2nd Edition)
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Combining Pattern Classifiers: Methods and Algorithms
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Linear combiners for classifier fusion: some theoretical and experimental results
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
Parallel consensual neural networks
IEEE Transactions on Neural Networks
Improving model accuracy using optimal linear combinations of trained neural networks
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Trainable fusion rules. I. Large sample size case
Neural Networks
Trainable fusion rules. II. Small sample-size effects
Neural Networks
Calligraphic Interfaces: Classifier combination for sketch-based 3D part retrieval
Computers and Graphics
Classifier ensemble selection using hybrid genetic algorithms
Pattern Recognition Letters
A theoretical framework for multiple neural network systems
Neurocomputing
Confidence based multiple classifier fusion in speaker verification
Pattern Recognition Letters
Increasing classification efficiency with multiple mirror classifiers
Expert Systems with Applications: An International Journal
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Machine Vision and Applications
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Combining Methods for Dynamic Multiple Classifier Systems
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Beyond clustering of array expressions
International Journal of Bioinformatics Research and Applications
Semi-supervised Co-update of Multiple Matchers
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
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
Fusion of Multiple Expert Annotations and Overall Score Selection for Medical Image Diagnosis
SCIA '09 Proceedings of the 16th Scandinavian Conference on Image Analysis
Towards a Linear Combination of Dichotomizers by Margin Maximization
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Expert Systems with Applications: An International Journal
IEEE Transactions on Neural Networks
An Anticorrelation Kernel for Subsystem Training in Multiple Classifier Systems
The Journal of Machine Learning Research
Bayesian analysis of linear combiners
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Classifier combining rules under independence assumptions
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On the diversity-performance relationship for majority voting in classifier ensembles
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Ensemble learning in linearly combined classifiers via negative correlation
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International Journal of Knowledge Engineering and Soft Data Paradigms
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ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Sparse ensembles using weighted combination methods based on linear programming
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Sentiment classification and polarity shifting
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Dynamic linear combination of two-class classifiers
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Pattern Recognition Letters
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AUC-Based linear combination of dichotomizers
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ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
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ANNPR'06 Proceedings of the Second international conference on Artificial Neural Networks in Pattern Recognition
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CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
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Expert Systems with Applications: An International Journal
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Information Fusion
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In this paper, a theoretical and experimental analysis of linear combiners for multiple classifier systems is presented. Although linear combiners are the most frequently used combining rules, many important issues related to their operation for pattern classification tasks lack a theoretical basis. After a critical review of the framework developed in works by Tumer and Ghosh [30], [31] on which our analysis is based, we focus on the simplest and most widely used implementation of linear combiners, which consists of assigning a nonnegative weight to each individual classifier. Moreover, we consider the ideal performance of this combining rule, i.e., that achievable when the optimal values of the weights are used. We do not consider the problem of weights estimation, which has been addressed in the literature. Our theoretical analysis shows how the performance of linear combiners, in terms of misclassification probability, depends on the performance of individual classifiers, and on the correlation between their outputs. In particular, we evaluate the ideal performance improvement that can be achieved using the weighted average over the simple average combining rule and investigate in what way it depends on the individual classifiers. Experimental results on real data sets show that the behavior of linear combiners agrees with the predictions of our analytical model. Finally, we discuss the contribution to the state of the art and the practical relevance of our theoretical and experimental analysis of linear combiners for multiple classifier systems.