The recently proposed “generalized min-max” (GMM) kernel [9] can be efficiently linearized, with direct applications in large-scale statistical learning and fast near neighbor search. The linearized GMM kernel was extensively compared in [9] with linearized radial basis function (RBF) kernel. On a large number of classification tasks, the tuning-free GMM kernel performs (surprisingly) well comp...

This paper demonstrates that modelling session variability during GMM training can improve the performance of a GMM supervector SVM speaker verification system. Recently, a method of modelling session variability in GMM-UBM systems has led to significant improvements when the training and testing conditions are subject to session effects. In this work, session variability modelling is applied d...

We describe a GMM-UBM-based evaluation system for pathologic voices that uses standard cepstral features. Per speaker one GMM is created and its components are used to create a so-called GMM supervector. The supervector of each speaker is labeled with the intelligibility values obtained by human evaluation and is used to train an SVR. We studied different GMM supervectors containing different G...

Strong anti glycolipid IgG responses can occur in humans and animals, but contrary to anti protein responses and anti glycoprotein responses, the exact mechanism of induction is unknown. We have previously shown that experimental immunization with the glycolipid glucose monomycolate (GMM) causes the development of specific T cell responses, but not of anti GMM antibodies. However, cattle natura...

Generalized Multiobjective Multitree model (GMMmodel) considering by the first time multitree-multicast load balancing with splitting in a multiobjective context. To solve the GMM-model, a multiobjective evolutionary algorithm (MOEA) inspired by the Strength Pareto Evolutionary Algorithm (SPEA) was proposed. In this paper, we extends the GMM-model to dynamic multicast groups (i.e., in which egr...