Democracy in neural nets: voting schemes for classification
Neural Networks
Improving regression estimation: Averaging methods for variance reduction with extensions to general convex measure optimization
Machine Learning
Error reduction through learning multiple descriptions
Machine Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Integrating Faces and Fingerprints for Personal Identification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing 2-tone images in grey-level parametric eigenspaces
Pattern Recognition Letters
A Simple Implementation of the Stochastic Discrimination for Pattern Recognition
Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
EuroCOLT '99 Proceedings of the 4th European Conference on Computational Learning Theory
Experiments with Classifier Combining Rules
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Data Complexity Analysis for Classifier Combination
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
Face recognition using independent component analysis and support vector machines
Pattern Recognition Letters - Special issue: Audio- and video-based biometric person authentication (AVBPA 2001)
A hidden markov model-based approach for face detection and recognition
A hidden markov model-based approach for face detection and recognition
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
A Hybrid Face Recognition Method using Markov Random Fields
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Combining classifiers for face recognition
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 3 (ICME '03) - Volume 03
Multi-Classifier Systems: Review and a roadmap for developers
International Journal of Hybrid Intelligent Systems
Eigenfeature Regularization and Extraction in Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Committee machines for facial-gender recognition
International Journal of Hybrid Intelligent Systems
Sparsity preserving projections with applications to face recognition
Pattern Recognition
A complete fuzzy discriminant analysis approach for face recognition
Applied Soft Computing
Ensemble of SVMs for improving brain computer interface p300 speller performances
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
Combinations of weak classifiers
IEEE Transactions on Neural Networks
Face recognition/detection by probabilistic decision-based neural network
IEEE Transactions on Neural Networks
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Individual classifiers that are fully trained are unstable especially when the database conditions are changed. Moreover, designing a unique classifier with the suitable parameters to achieve acceptable performance is a non-trivial task. Combined classifiers, which consist of a set of individually trained classifiers, are introduced to avoid the previous problems. There are two key issues in the combination of classifiers. The first issue is how to obtain the set of base classifiers to combine. The second issue is how to fuse the decisions of those classifiers. In this paper, weak Learning Vector Quantization (LVQ) neural networks have been used as base classifiers. Also, a new combination technique which is based on training-weighted voting is introduced. Other factors that greatly affect the performance of a combined classifier are related to the type of the individual classifiers, the training parameters, database size and nature, etc. These factors have been considered in the design of the proposed combined classifier. TWE has been experimentally tested on five standard face databases: Yale, ORL, Grimace, Faces94 and Faces95 and has demonstrated excellent performance. Analysis of the ensemble stability has shown promising results.