We propose a probabilistic generative model for multitask learning that exploits the cluster structure of the task parameters, and additionally imposes a low-rank constraint on the set of task parameters within each cluster. This leads to a sharing of statistical strengths of multiple tasks at two levels: (1) via cluster assumption, and (2) via a subspace assumption within each cluster. Our wor...

in this work, thermal degradation behavior of a fuel-rich energetic mixture containing epoxy binder was studied by thrmogravimetric analysis and differential scanning calorimetry under dynamic nitrogen atmosphere at different heating rates. variation of the thermal degradation activation energy of the mixture was evaluated by differential and integral isoconversional methods via akts software p...

  Stochastic models such as mixture models, graphical models, Markov random fields and hidden Markov models have key role in probabilistic data analysis. In this paper, we have learned Gaussian mixture model to the pixels of an image. The parameters of the model have estimated by EM-algorithm.   In addition pixel labeling corresponded to each pixel of true image is made by Bayes rule. In fact, ...

High dimensional spaces pose a serious challenge to the learning process. It is a combination of limited number of samples and high dimensions that positions many problems under the “curse of dimensionality”, which restricts severely the practical application of density estimation. Many techniques have been proposed in the past to discover embedded, locally-linear manifolds of lower dimensional...

In this paper we consider a Bayesian analysis of contingency tables allowing for the possibility that cells may have probability zero. In this sense we depart from standard log-linear modeling that implicitly assumes a positivity constraint. Our approach leads us to consider mixture models for contingency tables, where the components of the mixture, which we call model-instances, have distinct ...

Voice transformation is the process of transforming the characteristics of speech uttered by a source speaker, such that a listener would believe the speech was uttered by a target speaker. In this paper we address the problem of transforming voice quality. We do not attempt to transform prosody. Our system has two main parts corresponding to the two components of the source-filter model of spe...

The automatic recognition and classification of speech under stress has applications in behavioural and mental health sciences, human to machine communication and robotics. The majority of recent studies are based on a linear model of the speech signal. In this study, the nonlinear Teager Energy Operator (TEO) analysis was used to derive the classification features. Moreover, the TEO analysis w...

Statistical Machine Translation (SMT) systems specialized for one domain often perform poorly when applied to other domains. Domain adaptation techniques allow SMT models trained from a source domain with abundant data to accommodate different target domains with limited data. This paper evaluates the performance of two adaptive techniques based on log-linear and mixture models on data from the...

This article is a (slightly) modified version of Grün and Leisch (2008b), published in the Journal of Statistical Software. flexmix provides infrastructure for flexible fitting of finite mixture models in R using the expectation-maximization (EM) algorithm or one of its variants. The functionality of the package was enhanced. Now concomitant variable models as well as varying and constant param...

We describe a new approach to speech recognition, in which all Hidden Markov Model (HMM) states share the same Gaussian Mixture Model (GMM) structure with the same number of Gaussians in each state. The model is defined by vectors associated with each state with a dimension of, say, 50, together with a global mapping from this vector space to the space of parameters of the GMM. This model appea...