IEEE Transactions on Pattern Analysis and Machine Intelligence
Direct Gray-Scale Minutiae Detection In Fingerprints
IEEE Transactions on Pattern Analysis and Machine Intelligence
Classifier Combinations: Implementations and Theoretical Issues
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Biometric Recognition: Security and Privacy Concerns
IEEE Security and Privacy
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Neural Network Based Minutiae Filtering in Fingerprints
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
Feature selection using linear classifier weights: interaction with classification models
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
A neural network based multi-classifier system for gene identification in DNA sequences
Neural Computing and Applications
Pattern recognition and reading by machine
IRE-AIEE-ACM '59 (Eastern) Papers presented at the December 1-3, 1959, eastern joint IRE-AIEE-ACM computer conference
Genetic algorithms in classifier fusion
Applied Soft Computing
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Traditional artificial neural architectures possess limited ability to address the scale problem exhibited by a large number of distinct pattern classes and limited training data. To address these problems, this paper explores a novel advanced encoding scheme, which reduces both memory demand and execution time, and provides improved performance. The novel advanced encoding scheme known as the engine encoding, have been implemented in a multi-classifier, which combines the scaled probabilities, configuration information, and the discriminating abilities of the associated component classifiers. The problems of overloading and saturation experienced by traditional networks are solved by training the base classifiers on differing sub-sets of the required pattern classes and allowing the combiner classifier to derive a solution. Current Multi-classifier Systems are easily biased when trained on one class more often than another class, when patterns representing a class are very large compared to the rest, or when the multi-classifier depends on a certain fixed order of arrangement of pattern classes. A unique statistical arrangement method is hereby presented which aims to solve the bias problem. This statistical arrangement method also enhances independence of component classifiers. The system is demonstrated on the exemplar of fingerprint identification and utilizes a Weightless Neural System called the Enhanced Probabilistic Convergent Neural Network (EPCN) in a Multi-classifier System.