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The use of intelligent agents in the structure of multi-classifier systems has been investigated in order to overcome some drawbacks of these systems and, as a consequence, to improve the performance of such systems. As a result of this, the NeurAge system was proposed. This system has presented good results in some conventional (centralized) classification… (More)
Prediction of gender characteristics from iris images has been investigated and some successful results have been reported in the literature, but without considering performance for different iris features and classifiers. This paper investigates for the first time an approach to gender prediction from iris images using different types of features… (More)
One of the most important steps in the design of a multi-classifier system (MCS), also known as ensemble, is the choice of the components (classifiers). This step is very important to the overall performance of a MCS since the combination of a set of identical classifiers will not outperform the individual members. The ideal situation would be a set of… (More)
We describe seven patients from two Mexican families with congenital macroglossia. Comparative analysis of these cases and the patients previously described in literature, allows to confirm the notion that this isolated primary macroglossia is a distinct dominant condition.
The effectiveness with which individual identity can be predicted in, for example, an antiterrorist scenario can benefit from seeking a broad base of identity evidence. The issue of improving performance can be addressed in a number of ways, but system configurations based on integrating different information sources (often involving more than one biometric… (More)
This paper proposes NeurAge, an agent-based multi-classifier system for classification tasks. This system is composed of several neural classifiers (called neural agents) and its main aim is to overcome some drawbacks of multi-classifier systems and, as a consequence, to improve performance of such systems.