This paper proposes an unsupervised algorithm for learning a finite mixture model from
multivariate data. The adjective" unsupervised" is justified by two properties of the algorithm …

Clustering is a useful tool for finding structure in a data set. The mixture likelihood approach
to clustering is a popular clustering method, in which the EM algorithm is the most used …

Simple methods to choose sensible starting values for the EM algorithm to get maximum
likelihood parameter estimation in mixture models are compared. They are based on …

Clustering is a common unsupervised learning technique used to discover group structure in
a set of data. While there exist many algorithms for clustering, the important issue of feature …

A general problem of supervised remotely sensed image classification assumes prior
knowledge to be available for all the thematic classes that are present in the considered …

A number of recent methods developed for automatic classification of multiunit neural activity
rely on a Gaussian model of the variability of individual waveforms and the statistical …

We propose a genetic-based expectation-maximization (GA-EM) algorithm for learning
Gaussian mixture models from multivariate data. This algorithm is capable of selecting the …

Data clustering has received a lot of attention and numerous methods, algorithms and
software packages are available. Among these techniques, parametric finite-mixture models …

This chapter presents some of the Bayesian solutions to the different interpretations of
picking the “right” number of components in a mixture, before concluding on the ill-posed …

Electroencephalography (EEG) signals are complex dynamic phenomena that exhibit
nonlinear and nonstationary behaviors. These characteristics tend to undermine the …