In the past few years, accuracy in determining gender from iris images has increased significantly, approaching levels that make novel applications of this biometric technology feasible. In this paper, we report the gender ...

Multi-label learning handles datasets where each instance is associated with multiple labels, which are often correlated. As other machine learning tasks, multi-label learning also suffers from the curse of dimensionality, ...

One of the main challenges in applying machine learning algorithms to biological sequence data is how to numerically represent a sequence in a numeric input vector. Feature extraction techniques capable of extracting ...

We introduce Supervised Variational Relevance Learning (Suvrel), a variational method to determine metric tensors to define distance based similarity in pattern classification, inspired in relevance learning. The variational ...

The study of the neural code aims at deciphering how the nervous system maps external stimuli into neural activity-the encoding phase-and subsequently transforms such activity into adequate responses to the original ...

Feature selection concerns the task of finding the subset of features that are most relevant to some specific problem in the context of machine learning. During the last years, the problem of feature selection has been ...

In this paper, we report our extension of the use of feature selection based on mutual information and feature fusion to improve gender classification of face images. We compare the results of fusing three groups of features, ...