A Bayesian-based methodology is presented which automatically penalises over-complex models being tted to unknown data. We show that, with a Gaussian mixture model, the approach is able to select anòptimal' number of components in the model and so partition data sets.

Word embeddings are now a standard technique for inducing meaning representations for words. For getting good representations, it is important to take into account different senses of a word. In this paper, we propose a mixture model for learning multi-sense word embeddings. Our model generalizes the previous works in that it allows to induce different weights of different senses of a word. The...

The present research examines the differential impact of the importance of internally and externally sourced information and knowledge and its relationship to absorptive capacity and firm performance. In addition, this analysis deals directly with the unobservable heterogeneity amongst firms that is generally viewed as the raison d'être for a unique resource based perspective of organizational ...

Mixture models have been widely used in modeling of continuous observations. For the possibility to estimate the parameters of a mixture model consistently on the basis of observations from the mixture, identifiability is a necessary condition. In this study, we give some results on the identifiability of multivariate logistic mixture models.

This work proposes two approaches to improve the poolbased active learning strategy ’Multi-Class Probabilistic Active Learning ’ (McPAL) by using two kernel functions based on Gaussian mixture models (GMMs). One uses the kernels for the instance selection of the McPAL strategy, the second employs them in the classification step. The results of the evaluation show that using a different classifi...