We study gender discrimination of human faces using a combination of psychophysical classification and discrimination experiments together with methods from machine learning. We reduce the dimensionality of a set of face images using principal component analysis, and then train a set of linear classifiers on this reduced representation (linear support vector machines (SVMs), relevance vector ma...

This opinion piece takes Google's response to the so-called COVID-19 infodemic, as a starting point argue for need consider societal relevance complement other types of relevance. The authors maintain that if information science wants be discipline at forefront research on relevance, search engines, and their use, then community needs address itself challenges conditions commercial engines crea...

Previous studies have demonstrated the benefits of PLDA-SVM scoring with empirical kernel maps for i-vector/PLDA speaker verification. The method not only performs significantly better than the conventional PLDA scoring and utilizes the multiple enrollment utterances of target speakers effectively, but also opens up opportunity for adopting sparse kernel machines in PLDA-based speaker verificat...

The issue of Automatic Relevance Determination (ARD) has attracted attention over the last decade for the sake of efficiency and accuracy of classifiers, and also to extract knowledge from discriminant functions adapted to a given data set. Based on Learning Vector Quantization (LVQ), we recently proposed an approach to ARD utilizing genetic algorithms. Another approach is the Generalized Relev...

Abstract We explore the hypothesis that learning machines extract representations of maximal relevance, where relevance is defined as entropy energy distribution internal representation. show mutual information between representation a machine and features it extracts from data bounded below by relevance. This motivates our study models with relevance—that we call optimal machines—as candidates...