Building a new generation of handwriting recognition systems
Pattern Recognition Letters - Postal processing and character recognition
Decision Combination in Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical mixtures of experts and the EM algorithm
Neural Computation
A modified HME architecture for text-dependent speaker identification
IEEE Transactions on Neural Networks
Expert systems for time-varying biomedical signals using eigenvector methods
Expert Systems with Applications: An International Journal
Features extracted by eigenvector methods for detecting variability of EEG signals
Pattern Recognition Letters
Usage of eigenvector methods in implementation of automated diagnostic systems for ECG beats
Digital Signal Processing
Statistics over features for internal carotid arterial disorders detection
Computers in Biology and Medicine
Decision support systems for time-varying biomedical signals: EEG signals classification
Expert Systems with Applications: An International Journal
Features for analysis of electrocardiographic changes in partial epileptic patients
Expert Systems with Applications: An International Journal
Modified Mixture of Experts for Diabetes Diagnosis
Journal of Medical Systems
Implementation of automated diagnostic systems: ophthalmic arterial disorders detection case
International Journal of Systems Science
IEEE Transactions on Information Technology in Biomedicine - Special section on biomedical informatics
Cascaded and hierarchical neural networks for classifying surface images of marble slabs
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews - Special issue on information reuse and integration
A modified mixture of experts network structure for ECG beats classification with diverse features
Engineering Applications of Artificial Intelligence
Segmentation of abdominal organs from CT using a multi-level, hierarchical neural network strategy
Computer Methods and Programs in Biomedicine
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A novel connectionist method is proposed to simultaneously use diverse features in an optimal way for pattern classification. Unlike methods of combining multiple classifiers, a modular neural network architecture is proposed through use of soft competition among diverse features. Parameter estimation in the proposed architecture is treated as a maximum likelihood problem, and an Expectation-Maximization (EM) learning algorithm is developed for adjusting the parameters of the architecture. Comparative simulation results are presented for the real world problem of speaker identification.