We propose a new method for fitting mixture models that performs component selection and does not require external initialization. The novelty of our approach includes: a minimum ...
We show that a classifier based on Gaussian mixture models (GMM) can be trained discriminatively to improve accuracy. We describe a training procedure based on the extended Baum-W...
—We present solutions to two problems that prevent the effective use of population-based algorithms in clustering problems. The first solution presents a new representation for ...
Gauss mixtures have gained popularity in statistics and statistical signal processing applications for a variety of reasons, including their ability to well approximatea large cla...
We propose an algorithm to construct classification models with a mixture of kernels from labeled and unlabeled data. The derived classifier is a mixture of models, each based o...