Abstract. Classifiers based on Gaussian mixture models are good performers in many pattern recognition tasks. Unlike decision trees, they can be described as stable classifier: a s...
Jonas Richiardi, Andrzej Drygajlo, Laetitia Todesc...
We propose a novel Bayesian learning framework of hierarchical mixture model by incorporating prior hierarchical knowledge into concept representations of multi-level concept struc...
Efficient probability density function estimation is of primary interest in statistics. A popular approach for achieving this is the use of finite Gaussian mixture models. Based on...
— In this paper, a modified version of the complex directional pyramid (PDTDFB) is proposed. Unlike the previous approach, the new FB provides an approximately tight-frame decom...
This paper discusses a set of modifications regarding the use of the Bayesian Information Criterion (BIC) for the speaker diarization task. We focus on the specific variant of the...